IAO Prompting: Making Knowledge Flow Explicit in LLMs through Structured Reasoning Templates

Aissatou Diallo; Anthony Hunter; Antonis Bikakis; Luke Dickens; Rob Miller

REVIEW 4 major objections 5 minor 1 cited by

IAO Prompting: Making Knowledge Flow Explicit in LLMs through Structured Reasoning Templates

T0 review · 4 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read IAO prompting uses a structured Input-Action-Output template to make LLM knowledge flow explicit, yielding small accuracy gains and much clearer error traces.

desk verdict IAO prompting shows a plausible small accuracy gain over zero-shot CoT, but its headline transparency claim is unsupported and is undercut by the paper's own StrategyQA case study. read the letter →

arxiv 2502.03080 v1 pith:T5RDTPTC submitted 2025-02-05 cs.CL

Aissatou Diallo , Antonis Bikakis , Luke Dickens , Anthony Hunter , Rob Miller This is my paper · ORCID

classification cs.CL

keywords IAOpromptingchain-of-thoughtknowledgetransparencylargelanguagemodelsreasoningtemplateszero-shothallucinationdetectionverification

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

IAO prompting asks an LLM to format each reasoning step as a Subquestion, the Input knowledge it uses, the Action it applies, and the Output it produces. The paper claims this makes the knowledge flow visible enough to audit while also improving zero-shot accuracy over chain-of-thought baselines on seven reasoning datasets. The strongest support comes from the transparency side: when answers were wrong, human evaluators preferred the IAO trace for locating the mistake 73–87% of the time, and rated it more transparent and interpretable. If true, the result is a simple, domain-independent method for turning free-text reasoning into checkable knowledge steps.

What carries the argument

The central object is the IAO template: each reasoning step is a quadruple (Subquestion, Input, Action, Output). Subquestion breaks the problem into a knowledge query; Input declares which given facts or previously derived outputs are used; Action states the operation to be performed; Output records the newly derived knowledge, which becomes available as Input for subsequent steps. This explicit carry-forward of Output to Input is what turns a free-form rationale into a checkable trace of knowledge flow.

What would settle it

Generate an IAO trace, then re-run the prompt with only the Input line edited to a different quantity; if the model still produces the same Output as before across many examples, the declared Input fields are not causally steering the reasoning.

Watch

Extended reading notes

Core claim

The central claim of the paper is that prompting LLMs to format every reasoning step as Input-Action-Output makes the knowledge the model uses explicit and, as a side effect, improves zero-shot accuracy on a range of reasoning benchmarks. Across GSM8k, AQuA, StrategyQA, CommonsenseQA, Last Letter, Date Understanding, and Object Tracking, IAO beats zero-shot CoT almost everywhere; for example, GPT-4's GSM8k accuracy rises from 90.1% to 94.2% and PALM-2's Last Letter accuracy from 75.6% to 88.8%. The main stated payoff, however, is verifiability: in human evaluation, when both IAO and CoT answers were wrong, crowdworkers chose IAO as the better text for locating the mistake 73–87% of the time, and preferred IAO for transparency and interpretability. Ablations show the Output field carries much of the benefit: removing it drops average accuracy by 12 percentage points, while removing Subquestion, Input, or Action also hurts.

Load-bearing premise

The transparency argument rests on the assumption that the labels the model writes under Subquestion, Input, Action, and Output genuinely mirror how it retrieves and applies knowledge, rather than being plausible-sounding text generated after the fact.

Editorial extensions

If this is right

If IAO's gains hold, zero-shot reasoning can be made more auditable without any task-specific examples or multi-prompt planning.
The Output-to-Input carry-forward gives a natural slot for external tools such as a calculator or Python shell to verify arithmetic between steps.
The ablation result implies that simply asking a model to state its output is not enough; the declared Input and Action also matter for accuracy.
Because the template is domain-independent, it can be applied to any question-answering or reasoning setting where a free-text chain of thought is currently used.
The human-evaluation asymmetry, with IAO preferred for finding errors but CoT preferred when the answer is correct, suggests the two formats serve different purposes.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

An untested extension is to mine IAO traces automatically for knowledge gaps: a step whose Output contradicts the given facts, or whose Action is vague, could be flagged as a candidate hallucination site.
IAO traces could be fed back into the model for self-correction: when an Output does not match its Input or Action, the model could be prompted to redo that specific step.
The structured steps could serve as a distillation format, using IAO traces from a large model as training data to teach smaller models the same auditable reasoning style.
A useful separation test would compare IAO to CoT with matched output length to see how much of the accuracy gain comes from the explicit fields rather than from generally writing more text.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The empirical core of this paper is modest but real: IAO prompting, with its Subquestion/Input/Action/Output template, edges out zero-shot CoT on most of the tested reasoning benchmarks, and the ablation shows the components matter. The comparisons to L2M and P&S are fair, and the human evaluation, though small, is a sensible way to test whether structured chains help people spot errors. I'd believe the accuracy trend if the runs were more solid—the GPT-4 numbers come from a single run, no significance tests are reported, and the text contradicts its own tables twice (the AQuA "decrease" that Table 1 shows as a 2.1-point gain, and the GSM8K 82.3 vs 83.9 confusion). Those are fixable sloppiness, but they need cleaning up.

The real problem is the transparency claim. The paper says IAO makes knowledge flow explicit and supports verification, but the Input/Action/Output fields are just text the model writes; there is no check that they correspond to knowledge actually retrieved or used. The authors cite Turpin et al. on unfaithful CoT but never ask whether IAO chains suffer the same failure. Their own flagship StrategyQA example (Table 5) is damning: GPT-4 fills the Input fields with invented numbers (100,000 daily passengers, 7,610 arena seats) and the paper describes this as "transparent and verifiable." It isn't verification; it's a plausible-sounding guess presented inside a structured box. The human evaluation only shows that readers prefer IAO chains for locating mistakes, not that the chains are faithful traces—and when the answer is wrong, preferring a chain that confidently fabricates assumptions is not obviously a good outcome.

I would not desk-reject this. The method is simple, the accuracy gains are consistent enough to be worth investigating, and the ablation is helpful. A serious referee should push the authors to either drop the "transparency into stored knowledge" language or validate it with an external measure—e.g., testing whether IAO's Input fields match retrieved or known facts, or comparing against post-hoc rationalizations. The paper also needs the prompt template included in the appendix so others can reproduce the results without reverse-engineering it.

Bottom line: worth a round of review, but the central contribution should be reframed as a structured prompting method that sometimes improves accuracy and helps humans find errors in the chain, not as a window into how LLMs access knowledge.

Referee Report

4 major / 5 minor

Summary. The paper introduces IAO (Input-Action-Output) prompting, a zero-shot structured template that asks an LLM to decompose each reasoning step into Subquestion, Input, Action, and Output fields. The authors evaluate IAO against zero-shot CoT, Least-to-Most (L2M), and Plan-and-Solve (P&S) on arithmetic, commonsense, symbolic, and logical reasoning datasets using GPT-4 and PALM-2. They report accuracy gains for IAO over zero-shot CoT on most tasks, a two-stage extension, a few-shot variant, an ablation of template fields, and a human evaluation suggesting that IAO chains are easier to inspect for errors. The paper claims that IAO not only improves accuracy but also makes the model's stored-knowledge access and application explicit and verifiable.

Significance. If the accuracy results hold up statistically, IAO is a simple, domain-independent prompting method with modest but consistent gains over zero-shot CoT, and its structured output is plausibly helpful for human audit of wrong answers. The paper's distinctive claimed contribution, however, is the transparency of knowledge flow: that IAO fields reveal and verify how LLMs access stored knowledge. That claim is not established by the evidence, and the paper's own case study undercuts it. The strengths are the breadth of tasks, the inclusion of a human evaluation, the honest Limitations section, and the ablation analysis.

major comments (4)

[Case studies (Table 5); Abstract; Discussion] The central transparency claim is unsupported and is contradicted by the paper's own flagship example. In Table 5, GPT-4's IAO trace for the 30th Street Station / Dorton Arena question writes 'Let's say the daily passenger traffic at 30th Street Station is 100,000 people' and 'Let's assume Dorton Arena has a seating capacity of 7,610 people' into the Output fields, while the Input fields only state a need to find or estimate the data. These are invented placeholders, not retrieved stored knowledge, yet the text describes this as a 'transparent and verifiable solution.' The paper never validates the IAO-generated knowledge fields against any external ground truth, and the human evaluation only measures which format annotators prefer for spotting mistakes, not whether the fields are faithful. The risk of unfaithful rationalization is acknowledged via the citation to Turpin et al. (2024) but is never addressed for IAO. To support the transparency claim, the authors must either provide external validation of the knowledge fields (e.g., fact-checking the Input/Output entries against a knowledge base or human annotation) or explicitly reframe the contribution as 'structured reasoning text that humans find easier to audit.'
[Results (Arithmetic, PALM-2; Two-stage IAO)] The reported accuracy advantages are not supported by significance testing. GPT-4 results come from a single run, and PALM-2 results are averages of 3 runs without standard deviations. Several gains are small (e.g., 2.1 p.p. on AQuA in Table 1), so they may be within noise. Moreover, the text contradicts the tables in two places: the PALM-2 subsection states that AQuA accuracy decreased by 2.1 p.p. although Table 1 shows an increase from 61.8 to 63.9, and the two-stage IAO paragraph reports GSM8k accuracy of 82.3% for two-stage IAO while Table 6 shows 83.9%. These inconsistencies must be corrected, and the authors should report standard deviations and run significance tests (or at least confidence intervals) for the accuracy comparisons.
[IAO Prompting; Experimental Setup] The exact IAO prompt template is never given. The paper describes the four fields (Subquestion, Input, Action, Output) but does not provide the instruction text that elicits this structure from the models, nor the exact prompts used for the zero-shot CoT, L2M, and P&S baselines. Without the precise prompts, the method cannot be reproduced or the comparison fairly evaluated. The appendix examples show outputs but not the prompt that produced them.
[Human evaluation; Table 7] The human evaluation is underspecified and does not warrant the claim of 'significantly improves.' No details are given on the number of annotators per item, the annotation instructions, whether the order of the two chains was randomized, or inter-annotator agreement. The sample is small (10 correct and 10 wrong questions per dataset for three datasets), and no statistical test is reported for the preference percentages. This matters because the paper uses this evaluation as evidence for its transparency contribution; as reported, the results only indicate a subjective preference, not a measurable improvement in verification accuracy.

minor comments (5)

[Results, PALM-2 paragraph] The sentence 'There is a decrease in terms of percentage points (p.p), 2.1%, for the AQuA dataset' is confusing and contradicted by Table 1; the intended meaning appears to be an increase, and the phrasing should be clarified.
[Table 5] In the PALM-2 rows, the CoT chain states Dorton Arena has a seating capacity of 5,000, while the IAO chain in the same table states 10,000; the surrounding text says the model used '5,000 seating capacity.' This inconsistency is not discussed and further shows that the IAO 'Input' fields cannot be taken as reliable reports of stored knowledge.
[Ablation studies; Tables 3 and 8] The aggregation used for the 'Average' in Table 3 is not defined, and Table 8 only reports four of the seven datasets; the authors should specify which tasks and runs contribute to each ablation average.
[References; Compute cost] The BIG-bench collection is cited in the dataset section but is missing from the reference list, and the sentence in the compute-cost section ending 'justify the extra computational expense of two results in improved accuracy' is ungrammatical and should be rewritten.
[General] There are numerous small typos and spacing errors (e.g., 'the answer is(D)' in Table 4, 'Therefore, the answer isNone' in error examples). A careful proofreading pass is needed.

Circularity Check

1 steps flagged · score 4.0 of 10

Accuracy benchmarks are independent, but the headline transparency/verification claim is self-referential: IAO's Output fields are labeled 'verified knowledge' by the template and then cited as evidence of knowledge flow.

self definitional [Figure 1 caption; IAO Prompting section, components 1-4; Case studies, Table 5 discussion]
"Each step’s output becomes verified knowledge for subsequent reasoning. ... This structured decomposition enables us to trace knowledge flow, verify factual consistency, and identify potential knowledge gaps or misapplications."

The IAO template's Output field is defined as "new knowledge" produced by the model, and the caption labels it "verified knowledge" without any external check. The paper's transparency/verification claim then takes these self-generated fields as evidence that knowledge flow has been traced and verified. This is circular by construction: the Output is "verified" because it is an Output, and the subsequent verification claim reads the label back as confirmation. Table 5 makes the reduction visible: GPT-4 writes "Let's assume Dorton Arena has a seating capacity of 7,610 people" into Output, and the paper still calls the solution "transparent and verifiable" because the comparison is internally consistent.

full rationale

The numerical results (Tables 1-3, 6, 8) compare IAO against external zero-shot CoT, L2M, and P&S baselines on standard datasets, so there is no fitted-parameter or benchmark circularity. The only load-bearing circular step is the transparency/verification claim: Figure 1 defines each IAO Output as "verified knowledge," and the IAO section defines Input as "the relevant knowledge components available." The paper then treats the model's self-labeled Input/Action/Output text as a faithful trace of stored-knowledge access. No external probe, intervention, or fidelity check links these fields to the model's parameters, and Table 5 shows GPT-4 filling Output with invented values ("Let's say the daily passenger traffic at 30th Street Station is 100,000 people"; "Let's assume Dorton Arena has a seating capacity of 7,610 people") yet the paper calls this a "transparent and verifiable solution." This is a construct-validity gap in the knowledge-flow claim, not a circularity in the performance numbers; the paper also cites Turpin et al. on unfaithful CoT but never applies that risk to IAO. The text/Table 6 GSM8k two-stage discrepancy (82.3 vs 83.9) is a reporting error, not circularity.

Assumptions & free parameters 0 free parameters · 4 assumptions · 1 invented entities

The performance comparisons are benchmarked against external baselines, so no free parameters are fitted. The main burden is conceptual: the transparency claim assumes the LLM's structured output mirrors its internal knowledge flow, which is not externally validated. Human-preference data partially support the error-spotting claim, but they are self-reported and selected on questions where both methods agree in correctness.

assumptions (4)

domain assumption The LLM's IAO-formatted output is a faithful trace of its internal knowledge access and application.
The transparency and knowledge-verification claims assume that the fields the model writes (Input, Action, Output) reflect actual internal knowledge flow. This is asserted in the 'IAO Prompting' section and in the Discussion without external validation.
domain assumption Benchmark accuracy on the seven selected datasets measures reasoning ability.
The paper treats task accuracy as the performance metric, which is standard in NLP but is an assumption about the validity of the benchmarks as proxies for reasoning.
domain assumption Human preference for IAO on error-spotting questions measures ability to verify knowledge utilization and detect hallucinations.
The human evaluation collects subjective preference about which reasoning text is more useful for spotting mistakes, not a measured error-detection rate against ground truth.
domain assumption A single GPT-4 run at temperature 0 is representative of model performance.
GPT-4 results are reported from a single run, implicitly assuming negligible variance; no confidence intervals or repeated runs are provided.

invented entities (1)

Explicit knowledge-flow trace (the IAO Subquestion/Input/Action/Output sequence)
purpose: Claimed to reveal how the LLM accesses and applies stored knowledge at each reasoning step
The paper presents this trace as evidence of internal knowledge utilization, but there is no external measurement, such as probing or intervention, showing that the trace corresponds to actual model internals.

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Cite this review

Pith. "Pith review of IAO Prompting: Making Knowledge Flow Explicit in LLMs through Structured Reasoning Templates." pith.science (2026). https://pith.science/paper/T5RDTPTC

@misc{pith2026250203080,
  author       = {Pith},
  title        = {Pith review of: IAO Prompting: Making Knowledge Flow Explicit in LLMs through Structured Reasoning Templates},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/T5RDTPTC}},
  note         = {Machine review of arXiv:2502.03080}
}

read the original abstract

While Large Language Models (LLMs) demonstrate impressive reasoning capabilities, understanding and validating their knowledge utilization remains challenging. Chain-of-thought (CoT) prompting partially addresses this by revealing intermediate reasoning steps, but the knowledge flow and application remain implicit. We introduce IAO (Input-Action-Output) prompting, a structured template-based method that explicitly models how LLMs access and apply their knowledge during complex reasoning tasks. IAO decomposes problems into sequential steps, each clearly identifying the input knowledge being used, the action being performed, and the resulting output. This structured decomposition enables us to trace knowledge flow, verify factual consistency, and identify potential knowledge gaps or misapplications. Through experiments across diverse reasoning tasks, we demonstrate that IAO not only improves zero-shot performance but also provides transparency in how LLMs leverage their stored knowledge. Human evaluation confirms that this structured approach enhances our ability to verify knowledge utilization and detect potential hallucinations or reasoning errors. Our findings provide insights into both knowledge representation within LLMs and methods for more reliable knowledge application.

Figures

Figures reproduced from arXiv: 2502.03080 by the authors.

**Figure 2.** Comparison of knowledge application between IAO prompting and zero-shot CoT using PALM-2 on GSM8k. IAO’s [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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Reference graph

Works this paper leans on

41 extracted references · 39 canonical work pages · cited by 1 Pith paper

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What are the last letters of each word in”Pauline Kerry Jeannette Hope”? ”Pauline Kerry Jeannette Hope” Get the last letters of each word ”e y e e”

work page
[2]

”e y e e” Concatenate the letters

Concatenate the last letters of each word. ”e y e e” Concatenate the letters. ”eyee” GPT-4 CoT To solve this, we need to take the last letter of each word in the phrase ”Pauline Kerry Jeannette Hope” and then concatenate them,which means to link them together in a sequence.Here are the last letters of each word:- Pauline: e- Kerry: y- Jeannette: e- Hope: ...

work page
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The last letter is ”e”

What is the last letter of the third word ”Jeannette”? The word ”Jeannette”Identify the last letter of theword ”Jeannette”. The last letter is ”e”

work page
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The last letter is ”e”

What is the last letter of the fourth word ”Hope”? The word ”Hope”Identify the last letter of theword ”Hope”. The last letter is ”e”

work page
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The last letter is ”e”

What is the last letter of the first word ”Pauline”? The word ”Pauline”Identify the last letter of theword ”Pauline”. The last letter is ”e”

work page
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The last letter is ”y”

What is the last letter of the second word ”Kerry”? The word ”Kerry”Identify the last letter of theword ”Kerry”. The last letter is ”y”

work page
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The last letters ”e”, ”y”, ”e”, ”e”Concatenate the letters ”e”, ”y”, ”e”, ”e”in the order they were obtained.The concatenated result is”eyee”

Concatenate the last letters of each word. The last letters ”e”, ”y”, ”e”, ”e”Concatenate the letters ”e”, ”y”, ”e”, ”e”in the order they were obtained.The concatenated result is”eyee”. Table 13: Output examples from Last Letters dataset. The final results are in bold. QuestionToday’s meeting is rescheduled to 11 am tomorrow, 10/16/1924. What is the date ...

work page 1924
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What is the date 10 days ago? 10/16/1924 subtract 10 days from the given date 10/06/19242. What is the answer choice thatmatches the date 10 days ago?10/06/1924 compare the date 10 days agowith the answer choices (D)10/06/1924 GPT-4 CoT To find the date 10 days before 10/16/1924, we need to subtract 10 days from that date.Starting with 10/16/1924:1. If we...

work page 1924

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Subtract 10 days from the given date.The date 10 days before10/16/1924.To find the date 10 days before 10/16/1924, we need to count backwards day by day

What is the date 10 daysbefore 10/16/1924? The given date is 10/16/1924. Subtract 10 days from the given date.The date 10 days before10/16/1924.To find the date 10 days before 10/16/1924, we need to count backwards day by day. Since October has 31 days, subtracting 10 daysfrom...

1924
[13]

Bob has The Great Gatsby

What book does Bob have after the first swap?Alice has The Great Gatsby,Bob has The Odyssey.Bob and Alice swap books. Bob has The Great Gatsby
[14]

Bob and Claire swap books

What book does Bob have after the second swap?Bob has The Great Gatsby,Claire has Lolita. Bob and Claire swap books. Bob has The Great Gatsby
[15]

No more swaps

What book does Bob have at the end of the semester? Bob has The Great Gatsby. No more swaps. Bob hasThe Great Gatsby. GPT-4 CoT Let’s track the movement of the books step by step:1. Initially:- Alice has The Great Gatsby.- Bob has The Odyssey.- Claire has Lolita.2. After Alice...
[16]

Alice now has The Odyssey,Bob now has The Great Gatsby

What book does Bob have after he and Alice swap books?Alice has The Great Gatsby,Bob has The Odyssey.Swap the books betweenAlice and Bob. Alice now has The Odyssey,Bob now has The Great Gatsby
[17]

Swap the books betweenAlice and Claire

What book does Alice have after she and Claire swap books?Alice has The Odyssey,Claire has Lolita. Swap the books betweenAlice and Claire. Alice now has Lolita,Claire now has The Odyssey
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Bob now hasThe Odyssey,Claire now has The Great Gatsby

What book does Bob have after he and Claire swap books?Bob has The Great Gatsby,Claire has The Odyssey.Swap the books betweenBob and Claire. Bob now hasThe Odyssey,Claire now has The Great Gatsby. Table 15: Output examples from Object Tracking dataset. The final results are in...
[19]

Jamal has 52 - 8 = 44 apples

How many apples does Jamal have?Andrea has 8 more apples than Jamal.Andrea has 52 apples. Jamal has 52 - 8 = 44 apples. Jamal has 44 apples
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Jamal has 44 + 4 = 48 bananas

How many bananas does Jamal have?Jamal has 4 more bananas than apples.Jamal has 44 apples. Jamal has 44 + 4 = 48 bananas. Jamal has 48 bananas
[21]

Andrea has 48 / 2 = 24 bananas

How many bananas does Andrea have?Andrea has half as many bananas as Jamal.Jamal has 48 bananas. Andrea has 48 / 2 = 24 bananas. Andrea has 24 bananas
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There are168fruits in total

How many fruits are there in total?Andrea has 52 apples and 24 bananas.Jamal has 44 apples and 48 bananas.There are 52 + 24 + 44 + 48 = 168 fruits in total. There are168fruits in total. GPT-4 CoT First, let’s determine the number of apples and bananas Jamal has.According to th...
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The main idea of the question is thatthe man had difficulty keeping trackof conversations that he didn’t do something to

What is the main ideaof the question? The man tried to reply to thewoman, but he had difficultykeeping track of conversationsthat he didn’t do what to? Identify the main ideaof the question. The main idea of the question is thatthe man had difficulty keeping trackof conversati...
[24]

The missing word in the question is ”initiate”

What is the missingword in the question? The man tried to reply to thewoman, but he had difficultykeeping track of conversationsthat he didn’t do what to? Identify the missingword in the question. The missing word in the question is ”initiate”
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The answer to the question is(A)initiate

What is the answer tothe question? The man tried to reply to thewoman, but he had difficultykeeping track of conversationsthat he didn’t do what to? Choose the answer thatbest fits the question. The answer to the question is(A)initiate. GPT-4 CoT To solve this reasoning proble...
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Analyze the phrase to determinewhat aspect of conversation theman struggles with

What does the phrase”difficulty keeping track ofconversations” imply aboutthe man’s ability to engagein conversations? The man had difficulty keepingtrack of conversations. Analyze the phrase to determinewhat aspect of conversation theman struggles with. The man struggles with...
[27]

(A) initiate

Which answer choice isrelated to leading or starting aconversation? Answer choices (A) initiate(B) ignore (C) question(D) answer (E) ask Identify the answer choice thatmeans to begin or lead aconversation. (A) initiate
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Determine which action fromthe answer choices would be mostdifficult for someone who cannotkeep track of conversations

Does the difficulty in keepingtrack of conversations imply thatthe man did not do what? The man had difficulty keepingtrack of conversations; the mantried to reply to the woman. Determine which action fromthe answer choices would be mostdifficult for someone who cannotkeep tra...
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Since there are 24 hours in a day, 36 hours is equivalent to 1 day + 12hours
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The start of 2015 is at 00 : 00on January 1, 2015

2015
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Subtracting 1 day from January 1, 2015, takes us back to December 31, 2014

2015
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So, the current date and time, 36 hours before 2015, is 12 : 00(noon) on December 31, 2014

Since we need to subtract 12 more hours, and we are cur- rently at 00 : 00on December 31, 2014, we would still be on December 31, 2014, because we are not going back an entire day, just 12 hours into the previous day. So, the current date and time, 36 hours before 2015, is 12 ...

2014
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Therefore, among A through F, the answer is (B) 01/01/2015

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• Action: Assess the likelihood of outdoor activity based on known biographical information

Step 1: • Subquestion: Did John Muir spend a significant amount of time outdoors? • Input: Historical knowledge about John Muir’s life and activities. • Action: Assess the likelihood of outdoor activity based on known biographical information. • Output: Yes, John Muir spent a ...
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• Action: Correlate outdoor activity with sunlight expo- sure

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• Action: Relate sunlight exposure to vitamin D synthe- sis

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[2024]

arXiv preprint arXiv:2402.01761

Rethinking Interpretability in the Era of Large Lan- guage Models. arXiv preprint arXiv:2402.01761. Srivastava, A.; Rastogi, A.; Rao, A.; Shoeb, A. A. M.; Abid, A.; Fisch, A.; Brown, A. R.; Santoro, A.; Gupta, A.; Garriga- Alonso, A.; et al. 2023. Beyond the Imitation Game: Qu...

2023 arXiv

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.

[1] [1]

What are the last letters of each word in”Pauline Kerry Jeannette Hope”? ”Pauline Kerry Jeannette Hope” Get the last letters of each word ”e y e e”

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[2] [2]

”e y e e” Concatenate the letters

Concatenate the last letters of each word. ”e y e e” Concatenate the letters. ”eyee” GPT-4 CoT To solve this, we need to take the last letter of each word in the phrase ”Pauline Kerry Jeannette Hope” and then concatenate them,which means to link them together in a sequence.Here are the last letters of each word:- Pauline: e- Kerry: y- Jeannette: e- Hope: ...

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[3] [3]

The last letter is ”e”

What is the last letter of the third word ”Jeannette”? The word ”Jeannette”Identify the last letter of theword ”Jeannette”. The last letter is ”e”

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[4] [4]

The last letter is ”e”

What is the last letter of the fourth word ”Hope”? The word ”Hope”Identify the last letter of theword ”Hope”. The last letter is ”e”

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[5] [6]

The last letter is ”e”

What is the last letter of the first word ”Pauline”? The word ”Pauline”Identify the last letter of theword ”Pauline”. The last letter is ”e”

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[6] [7]

The last letter is ”y”

What is the last letter of the second word ”Kerry”? The word ”Kerry”Identify the last letter of theword ”Kerry”. The last letter is ”y”

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[7] [10]

The last letters ”e”, ”y”, ”e”, ”e”Concatenate the letters ”e”, ”y”, ”e”, ”e”in the order they were obtained.The concatenated result is”eyee”

Concatenate the last letters of each word. The last letters ”e”, ”y”, ”e”, ”e”Concatenate the letters ”e”, ”y”, ”e”, ”e”in the order they were obtained.The concatenated result is”eyee”. Table 13: Output examples from Last Letters dataset. The final results are in bold. QuestionToday’s meeting is rescheduled to 11 am tomorrow, 10/16/1924. What is the date ...

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[8] [11]

What is the date 10 days ago? 10/16/1924 subtract 10 days from the given date 10/06/19242. What is the answer choice thatmatches the date 10 days ago?10/06/1924 compare the date 10 days agowith the answer choices (D)10/06/1924 GPT-4 CoT To find the date 10 days before 10/16/1924, we need to subtract 10 days from that date.Starting with 10/16/1924:1. If we...

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