Pretrained LLMs Learn Multiple Types of Uncertainty | Paper Citation Record

Observation 5c039de0-4109-4a65-936b-248a7a039c2e · outbound

This paper cites Prompting as Probing: Using Language Models for Knowledge Base Construction.

Pretrained LLMs Learn Multiple Types of Uncertainty Prompting as Probing: Using Language Models for Knowledge Base Construction

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:44:25.858255Z

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Observation 51245314-00a6-4848-b957-943f6d61fccb · outbound

This paper cites QAMPARI : A benchmark for open-domain questions with many answers.

Pretrained LLMs Learn Multiple Types of Uncertainty QAMPARI : A benchmark for open-domain questions with many answers

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:28.126300Z

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Observation 55837c79-4c5b-4f59-b548-2927a1ff6ce1 · outbound

This paper cites Program Synthesis with Large Language Models.

Pretrained LLMs Learn Multiple Types of Uncertainty Program Synthesis with Large Language Models

Reference 3

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Observation 08fd4093-b0ed-4b7a-8273-98a4de8cc3fa · outbound

This paper cites The internal state of an LLM knows when it ' s lying.

Pretrained LLMs Learn Multiple Types of Uncertainty The internal state of an LLM knows when it ' s lying

Reference 4

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Observation 8274c78b-4f73-47b7-aecf-54dce2c8f273 · outbound

This paper cites Qwen Technical Report.

Pretrained LLMs Learn Multiple Types of Uncertainty Qwen Technical Report

Reference 5

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Observation 67d725e8-7cfd-4fb0-a517-3567be8bd052 · outbound

This paper cites Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models.

Pretrained LLMs Learn Multiple Types of Uncertainty Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models

Reference 6

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Observation e3072ad7-bc42-4155-af5c-8065ffb7a114 · outbound

This paper cites Language models are few-shot learners.

Pretrained LLMs Learn Multiple Types of Uncertainty Language models are few-shot learners

Reference 7

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Observation 1b2fea48-4c1d-4b35-b781-24d56ef69dcf · outbound

This paper cites Palm: Scaling language modeling with pathways.

Pretrained LLMs Learn Multiple Types of Uncertainty Palm: Scaling language modeling with pathways

Reference 8

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Observation 35f1b8d0-67fc-4d1a-ab6a-8ed0e083c3c7 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Pretrained LLMs Learn Multiple Types of Uncertainty Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T13:44:18.400204Z

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Observation 8b8fecdd-d94e-4a74-a64a-180ad5befbfd · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Pretrained LLMs Learn Multiple Types of Uncertainty Training Verifiers to Solve Math Word Problems

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T13:44:18.533583Z

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Observation f9929cd2-a5a7-488b-81cf-968d47936e7f · outbound

This paper cites Crawling the internal knowledge-base of language models.

Pretrained LLMs Learn Multiple Types of Uncertainty Crawling the internal knowledge-base of language models

Reference 11

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Observation b3393afe-9582-4179-9344-48b29dd5e8d7 · outbound

This paper cites LM vs LM : Detecting factual errors via cross examination.

Pretrained LLMs Learn Multiple Types of Uncertainty LM vs LM : Detecting factual errors via cross examination

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T13:44:18.923578Z

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Observation 415c3213-fd79-4b14-ab17-ef5eb07897ff · outbound

This paper cites Evaluating the ripple effects of knowledge editing in language models.

Pretrained LLMs Learn Multiple Types of Uncertainty Evaluating the ripple effects of knowledge editing in language models

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:27.955375Z

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Observation 0a83bd6a-9b4e-4f73-97f0-8155ba786676 · outbound

This paper cites I D on't K now: E xplicit modeling of uncertainty with an [IDK] token.

Pretrained LLMs Learn Multiple Types of Uncertainty I D on't K now: E xplicit modeling of uncertainty with an [IDK] token

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:27.707463Z

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Observation 300a6492-46f0-4b4c-a5de-29d0699d50a2 · outbound

This paper cites Calibration of pre-trained transformers.

Pretrained LLMs Learn Multiple Types of Uncertainty Calibration of pre-trained transformers

Reference 15

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Observation 05d875b1-4620-46fd-bad8-0104b8eb3bfa · outbound

This paper cites Evaluating factuality in text simplification.

Pretrained LLMs Learn Multiple Types of Uncertainty Evaluating factuality in text simplification

Reference 16

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Observation ca5436e7-86b1-43d0-aa65-b04b3b95967a · outbound

This paper cites The Llama 3 Herd of Models.

Pretrained LLMs Learn Multiple Types of Uncertainty The Llama 3 Herd of Models

Reference 17

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Observation dd0a13ac-abad-41cf-bb97-a5023f9988b0 · outbound

This paper cites Measuring and improving consistency in pretrained language models.

Pretrained LLMs Learn Multiple Types of Uncertainty Measuring and improving consistency in pretrained language models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T13:44:19.714512Z

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Observation 1e22d49e-7459-492e-9af4-37dfe19ff42b · outbound

This paper cites Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies.

Pretrained LLMs Learn Multiple Types of Uncertainty Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T13:44:19.853495Z

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Observation 08b89a74-157c-4aa5-98c9-902144d03b43 · outbound

This paper cites Transformer feed-forward layers are key-value memories.

Pretrained LLMs Learn Multiple Types of Uncertainty Transformer feed-forward layers are key-value memories

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T13:44:20.046321Z

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Observation a34120fe-0e6c-4d40-8ebe-cbe8856edbbf · outbound

This paper cites Dissecting recall of factual associations in auto-regressive language models.

Pretrained LLMs Learn Multiple Types of Uncertainty Dissecting recall of factual associations in auto-regressive language models

Reference 21

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Observation b7e715e6-c030-4de1-8a2d-7c3f90ec1e7e · outbound

This paper cites Weinberger.

Pretrained LLMs Learn Multiple Types of Uncertainty Weinberger

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:27.432417Z

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Observation d0767621-9cc3-40ec-b899-b19887780b2e · outbound

This paper cites Finding Neurons in a Haystack: Case Studies with Sparse Probing.

Pretrained LLMs Learn Multiple Types of Uncertainty Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 23

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Observation 5d726b10-d289-4ca8-914a-1778ac7a9cc2 · outbound

This paper cites Survey of hallucination in natural language generation.

Pretrained LLMs Learn Multiple Types of Uncertainty Survey of hallucination in natural language generation

Reference 24

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Observation 96f2dcc9-6ffc-433d-8d0a-bc409a625e5e · outbound

This paper cites Mistral 7B.

Pretrained LLMs Learn Multiple Types of Uncertainty Mistral 7B

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T13:44:20.543491Z

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Observation 4347d258-d18a-40e9-b757-62149a6e8724 · outbound

This paper cites How can we know when language models know? on the calibration of language models for question answering.

Pretrained LLMs Learn Multiple Types of Uncertainty How can we know when language models know? on the calibration of language models for question answering

Reference 27

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Observation ee1ea3bc-c423-453f-bf49-831a2b05a8ca · outbound

This paper cites TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension.

Pretrained LLMs Learn Multiple Types of Uncertainty TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T13:44:20.700534Z

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Observation f325ff1e-3772-4f47-a0ab-daec7094e55a · outbound

This paper cites Language Models (Mostly) Know What They Know.

Pretrained LLMs Learn Multiple Types of Uncertainty Language Models (Mostly) Know What They Know

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T13:44:20.765198Z

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Observation 3c6564ec-91bc-4d9d-af99-57ef7d96520b · outbound

This paper cites Large Language Models Must Be Taught to Know What They Don't Know.

Pretrained LLMs Learn Multiple Types of Uncertainty Large Language Models Must Be Taught to Know What They Don't Know

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T13:44:20.844620Z

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Observation 5c7582b5-7fc6-4825-a05b-19b5ea93fa72 · outbound

This paper cites Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation.

Pretrained LLMs Learn Multiple Types of Uncertainty Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T13:44:20.958690Z

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Observation 08e8271d-d1e7-4aa0-b41b-dbd696c0904a · outbound

This paper cites Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov.

Pretrained LLMs Learn Multiple Types of Uncertainty Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov

Reference 32

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Observation 712c5d70-c6b4-4c51-be07-768c0473deab · outbound

This paper cites Bloom: A 176b-parameter open-access multilingual language model.

Pretrained LLMs Learn Multiple Types of Uncertainty Bloom: A 176b-parameter open-access multilingual language model

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:27.213543Z

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Observation 9dba0352-6841-467c-9986-33f13e7a516b · outbound

This paper cites Teaching Models to Express Their Uncertainty in Words.

Pretrained LLMs Learn Multiple Types of Uncertainty Teaching Models to Express Their Uncertainty in Words

Reference 34

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Observation 54c3fa25-1e19-45d9-9632-34063a2366e6 · outbound

This paper cites Lin, Jacob Hilton, and Owain Evans.

Pretrained LLMs Learn Multiple Types of Uncertainty Lin, Jacob Hilton, and Owain Evans

Reference 35

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Observation db60cf89-58f8-4d1c-9e43-a3f6013ce692 · outbound

This paper cites On controlling fallback responses for grounded dialogue generation.

Pretrained LLMs Learn Multiple Types of Uncertainty On controlling fallback responses for grounded dialogue generation

Reference 36

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This paper cites When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories.

Pretrained LLMs Learn Multiple Types of Uncertainty When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories

Reference 37

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Observation 1d8d9356-38d9-496b-88b4-688d2cf232e2 · outbound

This paper cites On the Challenges and Opportunities in Generative AI.

Pretrained LLMs Learn Multiple Types of Uncertainty On the Challenges and Opportunities in Generative AI

Reference 38

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Observation 5ef83f34-f8a6-4e0b-8842-8caae6fb4959 · outbound

This paper cites On faithfulness and factuality in abstractive summarization.

Pretrained LLMs Learn Multiple Types of Uncertainty On faithfulness and factuality in abstractive summarization

Reference 39

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Observation 46696c03-4ce7-430c-b3af-9b0b3f7a6339 · outbound

This paper cites Locating and editing factual associations in GPT.

Pretrained LLMs Learn Multiple Types of Uncertainty Locating and editing factual associations in GPT

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:26.663717Z

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Observation 9d706bb7-18e5-48f0-aabb-b3f9327e1d6f · outbound

This paper cites A diverse corpus for evaluating and developing E nglish math word problem solvers.

Pretrained LLMs Learn Multiple Types of Uncertainty A diverse corpus for evaluating and developing E nglish math word problem solvers

Reference 41

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Observation 8264c148-4f31-47b6-8f04-10b35796b4a1 · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

Pretrained LLMs Learn Multiple Types of Uncertainty Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 42

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Observation 307c6eef-a58a-4956-b45f-77ede79c1c99 · outbound

This paper cites Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation.

Pretrained LLMs Learn Multiple Types of Uncertainty Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation

Reference 43

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Observation 32cbea88-c6f0-4b22-94d1-37b24dcb75f1 · outbound

This paper cites In-context Learning and Induction Heads.

Pretrained LLMs Learn Multiple Types of Uncertainty In-context Learning and Induction Heads

Reference 44

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This paper cites Training language models to follow instructions with human feedback.

Pretrained LLMs Learn Multiple Types of Uncertainty Training language models to follow instructions with human feedback

Reference 45

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Pretrained LLMs Learn Multiple Types of Uncertainty Unresolved cited work

Reference 46

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Pretrained LLMs Learn Multiple Types of Uncertainty Unresolved cited work

Reference 47

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This paper cites Language Models as Knowledge Bases?.

Pretrained LLMs Learn Multiple Types of Uncertainty Language Models as Knowledge Bases?

Reference 48

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This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Pretrained LLMs Learn Multiple Types of Uncertainty Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 49

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This paper cites Measuring attribution in natural language generation models.

Pretrained LLMs Learn Multiple Types of Uncertainty Measuring attribution in natural language generation models

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:26.453620Z

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Observation f909a57a-570c-4619-b4fc-a25c927dd92b · outbound

This paper cites How Much Knowledge Can You Pack Into the Parameters of a Language Model?.

Pretrained LLMs Learn Multiple Types of Uncertainty How Much Knowledge Can You Pack Into the Parameters of a Language Model?

Reference 51

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This paper cites C ommonsense QA : A question answering challenge targeting commonsense knowledge.

Pretrained LLMs Learn Multiple Types of Uncertainty C ommonsense QA : A question answering challenge targeting commonsense knowledge

Reference 52

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This paper cites Evaluating the factual consistency of large language models through news summarization.

Pretrained LLMs Learn Multiple Types of Uncertainty Evaluating the factual consistency of large language models through news summarization

Reference 53

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This paper cites Transformer memory as a differentiable search index.

Pretrained LLMs Learn Multiple Types of Uncertainty Transformer memory as a differentiable search index

Reference 54

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This paper cites LLaMA: Open and Efficient Foundation Language Models.

Pretrained LLMs Learn Multiple Types of Uncertainty LLaMA: Open and Efficient Foundation Language Models

Reference 55

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This paper cites Qwen2.5 Technical Report.

Pretrained LLMs Learn Multiple Types of Uncertainty Qwen2.5 Technical Report

Reference 56

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This paper cites Do Large Language Models Know What They Don't Know?.

Pretrained LLMs Learn Multiple Types of Uncertainty Do Large Language Models Know What They Don't Know?

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T13:44:23.893925Z

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This paper cites Narrowing the knowledge evaluation gap: Open-domain question answering with multi-granularity answers.

Pretrained LLMs Learn Multiple Types of Uncertainty Narrowing the knowledge evaluation gap: Open-domain question answering with multi-granularity answers

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T13:44:24.062339Z

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Observation 5a224dd2-d7a0-49cd-8093-f0200bd8a7ea · outbound

This paper cites Answering questions by meta-reasoning over multiple chains of thought.

Pretrained LLMs Learn Multiple Types of Uncertainty Answering questions by meta-reasoning over multiple chains of thought

Reference 59

Resolution: verified exact; doi, observed 2026-08-07T13:44:24.965880Z

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Observation bfc859f3-b125-485c-b920-50c6de9a8624 · outbound

This paper cites Mechanistic Understanding and Mitigation of Language Model Non-Factual Hallucinations.

Pretrained LLMs Learn Multiple Types of Uncertainty Mechanistic Understanding and Mitigation of Language Model Non-Factual Hallucinations

Reference 60

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Observation be239387-1e18-4ea2-9c20-1d4dd23353e6 · outbound

This paper cites Characterizing mechanisms for factual recall in language models.

Pretrained LLMs Learn Multiple Types of Uncertainty Characterizing mechanisms for factual recall in language models

Reference 61

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Observation 09bd273d-6236-44ca-87a8-5868bfe39dc0 · outbound

This paper cites Automatic evaluation of attribution by large language models.

Pretrained LLMs Learn Multiple Types of Uncertainty Automatic evaluation of attribution by large language models

Reference 62

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Observation 0f170296-6882-4fca-9975-eff6a062f94b · outbound

This paper cites R -tuning: Instructing large language models to say I don`t know'.

Pretrained LLMs Learn Multiple Types of Uncertainty R -tuning: Instructing large language models to say I don`t know'

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T13:44:24.465562Z

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Observation e7908ab5-c599-4b54-8003-10ebd0ef31a3 · outbound

This paper cites Instruction tuning for large language models: A survey.

Pretrained LLMs Learn Multiple Types of Uncertainty Instruction tuning for large language models: A survey

Reference 64

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Observation 3476bfdf-8908-4b94-b365-458d955c14d5 · outbound

This paper cites Codegeex: A pre-trained model for code generation with multilingual benchmarking on humaneval-x.

Pretrained LLMs Learn Multiple Types of Uncertainty Codegeex: A pre-trained model for code generation with multilingual benchmarking on humaneval-x

Reference 65

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Observation f6c8d9bf-243c-4b82-999f-5a8e0ba9463f · outbound

This paper cites RoMQA : A benchmark for robust, multi-evidence, multi-answer question answering.

Pretrained LLMs Learn Multiple Types of Uncertainty RoMQA : A benchmark for robust, multi-evidence, multi-answer question answering

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:26.094054Z

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