DocTalk: Scalable Graph-based Dialogue Synthesis for Enhancing LLM Conversational Capabilities

Binxuan Huang; Colin Lockard; Hamed Bonab; Haodong Wang; Jing Yang Lee; Ming Zeng; Nasser Zalmout; Ritesh Sarkhel; Sanket Lokegaonkar

REVIEW 4 major objections 5 minor 1 cited by

DocTalk: Scalable Graph-based Dialogue Synthesis for Enhancing LLM Conversational Capabilities

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read Continued pre-training on synthesized multi-turn, multi-topic conversations improves an LLM's context memory and understanding by up to 40% without degrading existing capabilities.

desk verdict The pipeline and dataset are real contributions, but the headline 40% claim rests on a 73-sample judge metric that never sees the dialogue history, so the empirical core is not yet established. read the letter →

arxiv 2507.05750 v1 pith:2GQ7772J submitted 2025-07-08 cs.CL

Jing Yang Lee , Hamed Bonab , Nasser Zalmout , Ming Zeng , Sanket Lokegaonkar , Colin Lockard , Binxuan Huang , Ritesh Sarkhel

show 1 more author

Haodong Wang

This is my paper · ORCID

classification cs.CL

keywords conversationaldatasynthesismulti-turndialoguecontinuedpre-trainingcontextmemorydocumentgraphLLM-as-a-judgemulti-topicconversations

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

The paper argues that large language models underperform in multi-turn conversation partly because their pre-training data is mostly continuous prose, not dialogue. To close that gap cheaply, it introduces a pipeline that converts clusters of related encyclopedia articles into long, multi-topic information-seeking conversations: a document graph selects related articles, a dialogue graph orders their paragraphs into assistant turns, and a language model generates only the user questions. The resulting corpus, DocTalk, contains over 730,000 conversations. Continued pre-training on DocTalk yields up to a 40% relative gain on two context-memory-and-understanding evaluations, while guardrail benchmarks stay roughly flat. If the result holds, the lesson is that the structure of conversational training data, not just its volume, can teach foundational multi-turn skills.

What carries the argument

The central machinery is a two-graph sampling process. Stage 1 builds a weighted directed document graph whose edges follow references between encyclopedia articles; a probabilistic random walk, with probabilities set by out-degree centrality, picks three related documents. Stage 2 splits those documents into paragraph segments, builds a fully connected dialogue graph, and weights each edge with a Conversational Reward model—a reranker fine-tuned on six dialogue corpora to score whether a candidate assistant utterance coherently follows the previous one. A random walk over this graph produces the assistant-side turn order, interleaving paragraphs across documents to create topic shifts and cross-turn coreference. Stage 3 prompts a separate LLM to write only the user questions that elicit each assistant utterance, keeping most of the corpus verbatim prose and cutting synthesis cost by roughly 70%.

What would settle it

A concrete test is to replace the dialogue-graph ordering with random or original-document ordering of the same paragraphs while keeping the same user-question generation and training budget; if the context-memory gains persist, then structured multi-topic ordering is not the active ingredient and the central claim fails.

Watch

Extended reading notes

Core claim

The central claim is that continued pre-training on a large corpus of synthesized multi-turn, multi-topic information-seeking dialogues improves an LLM's context memory and understanding. The load-bearing evidence is a continued-pretraining experiment on a 7-billion-parameter open-weight model: 8,000 steps on a mixture that is 25% DocTalk raises CoQA F1 to 0.38 against 0.34 for plain encyclopedia text and 0.36 for a single-topic dialogue baseline, and raises the LLM-judge loose intent-coverage score to 0.542 against 0.424 for plain text. Guardrail evaluations across knowledge, reasoning, fluency, and long-context tasks show no systematic decline. Ablations that remove the dialogue graph perform worse on both targeted metrics, which the paper takes as evidence that the multi-topic dialogue structure itself, not merely the presence of user-utterance text, causes the improvement.

Load-bearing premise

The two evaluation instruments are valid measures of context memory and understanding: turn-level word-overlap F1 on CoQA and an LLM judge scoring 73 shopping dialogues; if those scores do not reflect real conversational memory, the observed 40% gain does not support the paper's central claim.

Editorial extensions

If this is right

Pre-training with a 25% DocTalk mixture raises CoQA F1 from 0.34 (plain Wikipedia) to 0.38 and improves LLM-judge intent coverage from 0.424 to 0.542, with guardrail benchmarks roughly unchanged.
Both graph stages are load-bearing: removing the dialogue graph drops CoQA F1 below the plain-Wikipedia baseline, so the ordering mechanism, not just the user questions, drives the effect.
Using only the first 30 turns of each conversation improves precision and F1 after turn 8 compared with full conversations, suggesting later synthetic turns add noise.
DocTalk training also improves multi-hop long-context QA scores on MuSiQue and 2WikiQA, so the corpus's long-context structure transfers beyond pure dialogue.
The pipeline synthesizes only user questions, cutting generation cost by about 70% relative to full dialogue generation, which makes corpus construction scalable to hundreds of thousands of conversations.

Reading between the lines

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

Editorial inference: the same pipeline could be applied to domain-specific prose—medical, legal, or customer-support text—to build conversational pre-training data without expensive human annotation.
Editorial inference: the finding that the first-30-turns variant outperforms full conversations suggests an optimal dialogue length exists and that later turns, produced after the graph is nearly exhausted, may be the main quality bottleneck to fix next.
Editorial inference: extending the Conversational Reward model to condition on more than one preceding assistant utterance is a natural next step that could improve topic-transition quality and, in turn, later-turn performance.
Editorial inference: if the 40% gain replicates on other model scales and families, synthetic conversational pre-training could reduce reliance on scarce human chat logs as a source of multi-turn skill.

Share X Bluesky LinkedIn Reddit HN

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

Colleague,

First thing to know: the paper builds something real. The DocTalk pipeline turns multiple Wikipedia docs into long multi-topic dialogues without LLM-generating the assistant turns, and the released corpus (730k conversations, ~8B tokens) plus the graph-based synthesis design are new relative to Dialogue Inpainting, AutoConv, and SOLID. The CR model for transition scoring is a reasonable idea, the ablations isolating Stage 2 and Stage 3 are properly designed, and the human evaluation with Krippendorff's alpha is a genuine quality check. The cost analysis is also transparent.

The soft spot is exactly where the headline lives. The 'up to 40% gain in context memory and understanding' comes from a 73-sample LLM-as-a-judge shopping benchmark. Look at Appendix A.2, Figure 6: the judge prompt receives only the final response's intro/bullets and a manually pre-extracted user intent; it never sees the dialogue history. So the metric at best measures coverage of stated intents, not whether the model retrieved them from context. A model could produce generic content that happens to mention the intents and score well. The 40% figure is also selective: it's the Intro metric for DocTalk (0.424 vs Plain Wiki 0.305); DocTalk*, which wins on CoQA, scores 0.195 on the same metric. That inconsistency is not addressed.

CoQA, the more credible instrument, shows a ~12% relative F1 gain (0.38 vs 0.34) and DocTalk* at 0.40. But there are no error bars, no multiple seeds, no significance tests anywhere in the paper. Single-run differences of this size could be noise. The 'without compromising base performance' claim is also weakened by the paper's own guardrail table: DocTalk drops MMLU to 0.53 vs Plain Wiki's 0.55, and WikiText2 perplexity rises from 4.96 to 6.04. The paper discusses these, but calls them not significant without statistical support.

So: the pipeline and dataset deserve serious attention, but the central empirical claim is not yet established. A revision with seeds, confidence intervals, a standard multi-turn benchmark, and an honest framing of the 73-sample judge metric would make this a solid paper. As is, I'd send it to review, not desk-reject, because the method and release are valuable to the pre-training-data community. My own verdict would be conditional on major evaluation revisions.

Referee Report

4 major / 5 minor

Summary. This paper introduces DocTalk, a scalable pipeline that converts prose corpora into synthetic multi-turn, multi-topic information-seeking dialogues. The pipeline has three stages: (i) a document graph samples related Wikipedia articles, (ii) a dialogue graph interleaves and reorders document segments via a learned Conversational Reward (CR) model to form assistant utterances, and (iii) an LLM generates only the user questions, reducing cost and hallucination risk. The resulting corpus contains over 730k conversations. The authors continue pre-training Mistral-7B-v0.3 on DocTalk and report gains in context memory and understanding on CoQA and a custom LLM-as-a-judge shopping benchmark, claiming up to 40% relative improvement without compromising base performance.

Significance. The DocTalk dataset release, the three-stage pipeline, the 70% cost reduction estimate, and the ablation isolating the dialogue graph are useful contributions to data-centric pre-training. If the headline claim were fully supported, the work would be an important demonstration that synthetic conversational data can improve multi-turn capabilities at scale. The human evaluation of dialogue quality, the large corpus statistics, and the public dataset availability are also positive features. However, the strongest quantitative claim currently rests on an evaluation instrument that does not actually measure what it purports to measure, and the more credible CoQA signal is small and unreplicated.

major comments (4)

[§4.1.1 and Appendix A.2, Fig. 6] The LLM-as-a-judge setup used for Table 5 passes only the response introduction (or bullet points) and a pre-extracted user intent to the judge; it never passes the dialogue history. Consequently, the metrics defined in Eqs. (3)-(7) measure whether the final response mentions the target information, not whether the model retrieved that information from prior turns. Because the abstract's "up to 40% gain" comes from Table 5 (DocTalk Intro 0.424 vs Plain Wiki 0.305, +39%), the headline effect is not yet supported by this instrument. The authors should either redesign the judge to condition on the full dialogue history or demote Table 5 to a response-coverage analysis and lead with CoQA.
[Tables 4 and 5] All evaluation numbers come from a single continued-pretraining run with no standard errors, confidence intervals, or significance tests. The CoQA F1 differences supporting the central claim are small (DocTalk 0.38 vs Plain Wiki 0.34; DocTalk* 0.40 vs Plain Wiki 0.34). Without replication across seeds or at least a bootstrap/permutation assessment, these differences may be within run-to-run noise. Please report seed-level variance and a significance test for the main comparisons.
[Appendix A.2.3 and Table 5] The judge prompt was hand-tuned on 10 labeled examples, and the reported "at least 80% precision/recall" and "90% consistency" figures are not accompanied by a held-out sample, a description of the prompt-selection procedure, or a list of the 10 examples. On a 73-sample benchmark, a judge with 80-90% agreement can produce the observed differences by chance. The authors should provide a held-out judge calibration and a confusion matrix, and ideally use multiple judge models.
[Table 6 and §5] The claim "without compromising base performance" is not supported by the reported guardrails: DocTalk yields MMLU 0.53 vs Plain Wiki 0.55 and WikiText2 perplexity 6.04 vs 4.96, and no uncertainty estimates are given. These differences are not discussed as potential degradation, and the phrase "no significant decline" in §5 is used without a statistical test. The authors should either add variance estimates and a clear criterion for what counts as "compromise" or soften the claim accordingly.

minor comments (5)

[§4.1.1] The text says "we curtate a 73-sample" and should read "we curated a 73-sample."
[Tables 4 and 6 and Figure 4] The table header "A ve # words" should be "Avg # words", the header "w/o Stage 2 3" should be "w/o Stage 2 & 3", and the label "Raw Wiki" in Figure 4 should be "Plain Wiki" to match Table 4.
[§4.1.1] The CoQA corpus is cited as "(Adlakha et al., 2022)"; CoQA is due to Reddy et al. (2018), while Adlakha et al. (2022) is TopiOCQA. Please correct the reference.
[§5 and Table 6] The text refers to "2Wik iHotPotQA" but the table uses "2WikiQA"; please use one consistent name, such as 2WikiMultiHopQA.
[Appendix A.2.2] The notation "Let intent denote the set..." is confusing because "intent" is used both as the set and as the variable name; please clarify with a distinct symbol for the set of user intents.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: DocTalk's synthesis, CR-model training, and CoQA evaluation are externally grounded; the 40% headline rests on a judge metric of questionable construct validity, but that is a measurement concern, not a reduction-by-construction.

full rationale

The paper's derivation chain is not circular. The Conversational Reward model is trained on external dialogue corpora (HybriDialogue, InScitic, TopiOCQA, Wizard of Wikipedia, ShareGPT, UltraChat) with held-out test sets, and it is not trained on DocTalk or on the CoQA benchmark. DocTalk itself is generated from Wikipedia via the GDoc/GDial graph traversal plus LLM-generated user utterances, so the training data does not include the evaluation targets. The CoQA evaluation is an external, standard conversational QA benchmark, and the 73-sample LLM-as-a-judge benchmark is a separate curated set; neither is used to construct the training data or to optimize the pre-trained models. The primary validity concern is that the LLM-as-a-judge prompt in Appendix A.2 sees only the final response component and a manually pre-extracted user intent, not the dialogue history, so the 'Intro' metric may measure response coverage rather than context memory; however, this is a construct-validity and measurement concern, not a circularity, because the metric is not fed back into the pipeline and the improvement is not forced by construction. The DocTalk* 30-turn cutoff and the 25/5/70 data mixture are design/ablation choices applied symmetrically across baselines, not fitted parameters that mathematically determine the reported gains. There is no load-bearing self-citation or uniqueness theorem imported from the authors' prior work; the citations to WIKIR, bge-reranker, and standard benchmarks are independent. One bibliographic defect exists: Section 4.1.1 attributes the CoQA corpus to Adlakha et al. (2022), whose cited paper is actually TopiOCQA, but this is a reference error with no bearing on circularity.

Assumptions & free parameters 7 free parameters · 6 assumptions · 2 invented entities

The central claim rests on several hand-chosen design parameters (data mixture, graph sizes, training budget, DocTalk* truncation) and on domain assumptions about the validity of Wikipedia as a source, the transferability of the CR model, and the ability of CoQA F1 and the small judge set to measure context memory. No physical or mathematical constants are involved. The CR model and DocTalk corpus are trained or curated artifacts with independent evaluation, so they are not ungrounded postulates.

free parameters (7)

data mixture percentages = 25% conversational / 5% books / 70% web
Chosen by hand for all experiments; the central results depend on this allocation of DocTalk versus standard pre-training data.
document sample size n = 3
Stage 1 samples three related documents per conversation, which determines topical diversity and conversation length.
document graph depth = 3
GDoc expansion stops at depth 3, affecting the relatedness and connectedness of sampled documents.
qrels thresholds = minimum 10 qrels, limit 20 qrels
Documents with fewer than 10 qrels are filtered and edges are limited to 20 qrels, shaping the document graph.
CR model negative mining k = k=2 selected by MRR
The reward model is trained with k=1,2,3 and k=2 is chosen because it gives the highest MRR, affecting dialogue graph edge weights.
DocTalk* turn truncation = 30 turns
The DocTalk* variant keeps only the first 30 turns, triplicated, chosen after hypothesizing that later turns are lower quality.
continued pre-training budget = 8000 steps, 30B tokens, LR 3e-5 to 3e-6
The training schedule is hand-selected and is not varied, so all results are tied to this budget.

assumptions (6)

domain assumption Wikipedia articles and their hyperlink graph provide a sufficient source domain for learning multi-turn conversational structure.
Stage 1 builds GDoc from Wikipedia qrels; if articles are too different from real user conversations, the pre-training gains may not transfer.
domain assumption Out-degree centrality is a valid proxy for document informativeness and conversational value.
Edge weights in GDoc use out-degree, following Chhabra et al. 2021, but the paper does not validate this proxy against downstream performance.
domain assumption The CR model, fine-tuned on 4,240 external conversations, generalizes to score coherence between long Wikipedia paragraph segments.
Section 2.2; the entire dialogue graph traversal depends on CR scores, yet CR training data is shorter and smaller than DocTalk segments.
domain assumption LLM-generated user utterances are natural and specific enough for pre-training to transfer to real dialogue.
Stage 3 uses Mistral to generate questions from assistant utterances; the paper acknowledges reduced naturalness and relies on post-training to fix it.
domain assumption CoQA word-overlap F1 and the 73-sample judge metric measure the intended constructs of context memory and understanding.
The central evaluation rests on these instruments, with only a 10-sample judge tuning for validation.
domain assumption The 30B-token continued pre-training budget is sufficient to induce measurable dialogue-specific improvements.
Training for 8000 steps may not saturate; all results come from a single checkpoint, so conclusions are restricted to this budget.

invented entities (2)

Conversational Reward (CR) model independent evidence
purpose: Assigns coherence scores to candidate next assistant utterances in the dialogue graph traversal.
The CR model is trained on external dialogue data and assessed with MRR on held-out conversations (Table 1), giving it an independent empirical handle.
DocTalk dataset independent evidence
purpose: Large-scale synthetic pre-training corpus of 730k multi-turn, multi-topic information-seeking dialogues.
Publicly released on HuggingFace and evaluated by human raters and downstream training, so it is a concrete artifact rather than a postulated entity.

how reviews work

0 comments

Cite this review

Pith. "Pith review of DocTalk: Scalable Graph-based Dialogue Synthesis for Enhancing LLM Conversational Capabilities." pith.science (2026). https://pith.science/paper/2GQ7772J

@misc{pith2026250705750,
  author       = {Pith},
  title        = {Pith review of: DocTalk: Scalable Graph-based Dialogue Synthesis for Enhancing LLM Conversational Capabilities},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2GQ7772J}},
  note         = {Machine review of arXiv:2507.05750}
}

read the original abstract

Large Language Models (LLMs) are increasingly employed in multi-turn conversational tasks, yet their pre-training data predominantly consists of continuous prose, creating a potential mismatch between required capabilities and training paradigms. We introduce a novel approach to address this discrepancy by synthesizing conversational data from existing text corpora. We present a pipeline that transforms a cluster of multiple related documents into an extended multi-turn, multi-topic information-seeking dialogue. Applying our pipeline to Wikipedia articles, we curate DocTalk, a multi-turn pre-training dialogue corpus consisting of over 730k long conversations. We hypothesize that exposure to such synthesized conversational structures during pre-training can enhance the fundamental multi-turn capabilities of LLMs, such as context memory and understanding. Empirically, we show that incorporating DocTalk during pre-training results in up to 40% gain in context memory and understanding, without compromising base performance. DocTalk is available at https://huggingface.co/datasets/AmazonScience/DocTalk.

Figures

Figures reproduced from arXiv: 2507.05750 by the authors.

**Figure 3.** First 5 turns of a DocTalk conversation based on 3 Wikipedia articles: “Toronto General Hospital” (in orange), “University of Toronto Faculty of Medicine” (in blue), and “United Health Network” (in green). tions. The primary aim of our conversation synthesis pipeline is to generate a large-scale dataset that fundamentally improves context memory and understanding in multi-turn conversations during pre-training, emp… view at source ↗

**Figure 4.** Turn-level F1, precision, and recall plots. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

Figures from the paper (8 more)

**Figure 5.** Figure 5: Average general knowledge, reasoning, fluency, and long context scores evaluated every 2000 steps. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

**Figure 6.** Figure 6: Sample from the LLM-as-a-judge evaluation dataset. [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]

**Figure 7.** Figure 7: Sample from the LLM-as-a-judge evaluation dataset. [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]

**Figure 8.** Figure 8: Prompt template for user utterance generation in Stage 3. [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]

**Figure 9.** Figure 9: Excerpt of a DocTalk conversation based on 3 Wikipedia articles: ‘Joe Biden” (in orange), “George H.W. Bush” (in blue), and “Bill Gates” (in green). A.5 DocTalk Sample Samples from DocTalk are provided in [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]

**Figure 10.** Figure 10: First 10 turns of a DocTalk conversation based on 3 Wikipedia articles: “Toronto General Hospital” (in orange), “University of Toronto Faculty of Medicine” (in blue), and “United Health Network” (in green) [PITH_FULL_IMAGE:figures/full_fig_p017_10.png]

**Figure 11.** Figure 11: Samples of responses generated during the CoQA evaluation. Each example displays the outputs from [PITH_FULL_IMAGE:figures/full_fig_p018_11.png]

**Figure 12.** Figure 12: Questionnaire used for human evaluation. The questionnaire assesses dialogue quality through six criteria: [PITH_FULL_IMAGE:figures/full_fig_p020_12.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs
cs.CL 2026-07 conditional novelty 6.0 of 10

HSS-Synth generates 230k instruction-tuning samples for 14 humanities/social-science fields and reports state-of-the-art fine-tuning results on 16 benchmarks.

Reference graph

Works this paper leans on

13 extracted references · 7 canonical work pages · cited by 1 Pith paper

[1]

The ﬁnal score for a given dialogue is calculated as follows: (total number of responses - number of irrelevant responses)/total number of responses

Are the assistant’s responses contextually relevant to the preceding user utterance? Participants will be told to count the number of irrelevant responses. The ﬁnal score for a given dialogue is calculated as follows: (total number of responses - number of irrelevant responses)/total number of responses

work page
[2]

The New York Times

The customer is looking for a smart thermostat with the newest energy-saving features. Figure 7: Sample from the LLM-as-a-judge evaluation dataset. Given an utterance by a helpful assistant, generate a user utterance which consists of a question which elicits the assistant utterance. The question should be as specific as possible. Assistant Utterance: {ut...

work page 2000
[3]

In Proceedings of the 60th Annual Meet- ing of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8410–8423, Dublin, Ireland

PPT: Pre-trained prompt tuning for few-shot learning. In Proceedings of the 60th Annual Meet- ing of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8410–8423, Dublin, Ireland. Association for Computational Linguistics. Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021a...

arXiv 2009
[4]

Does the conversation feel natural and cohesive, resembling an information seeking conversation between a human and a LLM? Participants will be instructed to choose the option that best characterizes each dialogue. The ﬁnal score will be determined by averaging the scores assigned to each option (e.g., option 4 is scored as 4, option 3 as 3, and so on) ac...

work page
[5]

A pretrainer’s guide to training data: Measur- ing the effects of data age, domain coverage, quality, & toxicity. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies (Volume 1: Long Papers), pages 3245–3276. Stephen Merity, Caiming Xiong, James Bradbury, and Ri...

arXiv 2024
[6]

The ﬁnal score for a given dialogue is calculated as follows: (total number of topic shifts - number of illogical/incoherent topic shifts)/total number of topic shifts

Are the topical shifts logical and coherent within the context of the conversation? Participants will be told to count the number of illogical/incoherent topic shifts. The ﬁnal score for a given dialogue is calculated as follows: (total number of topic shifts - number of illogical/incoherent topic shifts)/total number of topic shifts. Not applicable to Wi...

work page
[9]

The ﬁnal score for a given dialogue is calculated as follows: (total number of user utterances - number of generic/imprecise user utterances)/total number of user utterances

Are the user utterances speciﬁc and precise? Participants will be told to count the number of generic and vague responses. The ﬁnal score for a given dialogue is calculated as follows: (total number of user utterances - number of generic/imprecise user utterances)/total number of user utterances

work page
[10]

The ﬁnal score for a given dialogue is calculated as follows: (total number of user utterances - number of erroneous/awkward user utterances)/total number of user utterances

Do the user utterances contain grammatical errors or awkward phrasing that disrupt understanding? Participants will be told to count the number of utterances with grammatical errors and awkward phrasing. The ﬁnal score for a given dialogue is calculated as follows: (total number of user utterances - number of erroneous/awkward user utterances)/total numbe...

work page

Show all 13 references

[12]

Are the topics covered in this conversation related to one another? Participants will be told to refer to the wikipedia article titles, and instructed to choose the option that best characterizes each dialogue. The ﬁnal score will be determined by averaging the scores assigned...
[2002]

JASIST, 53:639–652

Multitasking information seeking and search- ing processes. JASIST, 53:639–652. Mirac Suzgun, Nathan Scales, Nathanael Schärli, Se- bastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V . Le, Ed H. Chi, Denny Zhou, and Jason Wei. 2022. Challenging big-bench ta...

2022 arXiv
[2021]

Preprint, arXiv:2110.14168

Training verifiers to solve math word prob- lems. Preprint, arXiv:2110.14168. Zhuyun Dai, Arun Tejasvi Chaganty, Vincent Zhao, Aida Amini, Qazi Mamunur Rashid, Mike Green, and Kelvin Guu. 2022. Dialog inpainting: Turning documents into dialogs. Preprint, arXiv:2205.09073. Emil...

2022 arXiv
[2022]

Preprint, arXiv:2110.00768

Topiocqa: Open-domain conversational ques- tion answering with topic switching. Preprint, arXiv:2110.00768. Alon Albalak, Yanai Elazar, Sang Michael Xie, Shayne Longpre, Nathan Lambert, Xinyi Wang, Niklas Muennighoff, Bairu Hou, Liangming Pan, Hae- won Jeong, Colin Raffel, Shi...

arXiv
[2024]

Preprint, arXiv:2406.04770

Wildbench: Benchmarking llms with chal- lenging tasks from real users in the wild. Preprint, arXiv:2406.04770. Ruibo Liu, Jerry Wei, Fangyu Liu, Chenglei Si, Yanzhe Zhang, Jinmeng Rao, Steven Zheng, Daiyi Peng, Diyi Yang, Denny Zhou, and Andrew M. Dai. 2024. Best practices and...

2024 arXiv

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

The ﬁnal score for a given dialogue is calculated as follows: (total number of responses - number of irrelevant responses)/total number of responses

Are the assistant’s responses contextually relevant to the preceding user utterance? Participants will be told to count the number of irrelevant responses. The ﬁnal score for a given dialogue is calculated as follows: (total number of responses - number of irrelevant responses)/total number of responses

work page

[2] [2]

The New York Times

The customer is looking for a smart thermostat with the newest energy-saving features. Figure 7: Sample from the LLM-as-a-judge evaluation dataset. Given an utterance by a helpful assistant, generate a user utterance which consists of a question which elicits the assistant utterance. The question should be as specific as possible. Assistant Utterance: {ut...

work page 2000

[3] [3]

In Proceedings of the 60th Annual Meet- ing of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8410–8423, Dublin, Ireland

PPT: Pre-trained prompt tuning for few-shot learning. In Proceedings of the 60th Annual Meet- ing of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8410–8423, Dublin, Ireland. Association for Computational Linguistics. Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021a...

arXiv 2009

[4] [4]

Does the conversation feel natural and cohesive, resembling an information seeking conversation between a human and a LLM? Participants will be instructed to choose the option that best characterizes each dialogue. The ﬁnal score will be determined by averaging the scores assigned to each option (e.g., option 4 is scored as 4, option 3 as 3, and so on) ac...

work page

[5] [5]

A pretrainer’s guide to training data: Measur- ing the effects of data age, domain coverage, quality, & toxicity. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies (Volume 1: Long Papers), pages 3245–3276. Stephen Merity, Caiming Xiong, James Bradbury, and Ri...

arXiv 2024

[6] [6]

The ﬁnal score for a given dialogue is calculated as follows: (total number of topic shifts - number of illogical/incoherent topic shifts)/total number of topic shifts

Are the topical shifts logical and coherent within the context of the conversation? Participants will be told to count the number of illogical/incoherent topic shifts. The ﬁnal score for a given dialogue is calculated as follows: (total number of topic shifts - number of illogical/incoherent topic shifts)/total number of topic shifts. Not applicable to Wi...

work page

[7] [9]

The ﬁnal score for a given dialogue is calculated as follows: (total number of user utterances - number of generic/imprecise user utterances)/total number of user utterances

Are the user utterances speciﬁc and precise? Participants will be told to count the number of generic and vague responses. The ﬁnal score for a given dialogue is calculated as follows: (total number of user utterances - number of generic/imprecise user utterances)/total number of user utterances

work page

[8] [10]

The ﬁnal score for a given dialogue is calculated as follows: (total number of user utterances - number of erroneous/awkward user utterances)/total number of user utterances

Do the user utterances contain grammatical errors or awkward phrasing that disrupt understanding? Participants will be told to count the number of utterances with grammatical errors and awkward phrasing. The ﬁnal score for a given dialogue is calculated as follows: (total number of user utterances - number of erroneous/awkward user utterances)/total numbe...

work page