From Queries to Criteria: Understanding How Astronomers Evaluate LLMs | Paper Citation Record

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This paper cites Kurtz, Edwin A.

From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Kurtz, Edwin A

Reference 1

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This paper cites Henneken, Carolyn S.

From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Henneken, Carolyn S

Reference 2

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This paper cites OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs.

From Queries to Criteria: Understanding How Astronomers Evaluate LLMs OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs

Reference 3

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This paper cites Search B ots: User engagement with ChatBots during collaborative search.

From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Search B ots: User engagement with ChatBots during collaborative search

Reference 4

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This paper cites Embedding search into a conversational platform to support collaborative search.

From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Embedding search into a conversational platform to support collaborative search

Reference 5

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This paper cites The effects of system initiative during conversational collaborative search.

From Queries to Criteria: Understanding How Astronomers Evaluate LLMs The effects of system initiative during conversational collaborative search

Reference 6

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This paper cites Towards Reproducible LLM Evaluation: Quantifying Uncertainty in LLM Benchmark Scores.

From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Towards Reproducible LLM Evaluation: Quantifying Uncertainty in LLM Benchmark Scores

Reference 7

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This paper cites Bowman and George Dahl.

From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Bowman and George Dahl

Reference 8

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This paper cites Toward algorithmic accountability in public services: A qualitative study of affected community perspectives on algorithmic decision-making in child welfare services.

From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Toward algorithmic accountability in public services: A qualitative study of affected community perspectives on algorithmic decision-making in child welfare services

Reference 9

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Unresolved cited work

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This paper cites Fabbri, Wojciech Kry \'s ci \'n ski, Bryan McCann, Caiming Xiong, Richard Socher, and Dragomir Radev.

From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Fabbri, Wojciech Kry \'s ci \'n ski, Bryan McCann, Caiming Xiong, Richard Socher, and Dragomir Radev

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Unresolved cited work

Reference 12

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This paper cites Enabling large language models to generate text with citations.

From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Enabling large language models to generate text with citations

Reference 13

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This paper cites What can large language models do in chemistry? A comprehensive benchmark on eight tasks.

From Queries to Criteria: Understanding How Astronomers Evaluate LLMs What can large language models do in chemistry? A comprehensive benchmark on eight tasks

Reference 14

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This paper cites Measuring mathematical problem solving with the math dataset.

From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Measuring mathematical problem solving with the math dataset

Reference 15

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Iyer, Mikaeel Yunus, Charles O’Neill, Christine Ye, Alina Hyk, Kiera McCormick, Ioana Ciucă, John F

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Unresolved cited work

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This paper cites On scientific understanding with artificial intelligence.

From Queries to Criteria: Understanding How Astronomers Evaluate LLMs On scientific understanding with artificial intelligence

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This paper cites Wikibench: Community-driven data curation for AI evaluation on W ikipedia.

From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Wikibench: Community-driven data curation for AI evaluation on W ikipedia

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This paper cites Chapter 8 - interviews and focus groups.

From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Chapter 8 - interviews and focus groups

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This paper cites H alu E val: A large-scale hallucination evaluation benchmark for large language models.

From Queries to Criteria: Understanding How Astronomers Evaluate LLMs H alu E val: A large-scale hallucination evaluation benchmark for large language models

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Unresolved cited work

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This paper cites Rethinking Model Evaluation as Narrowing the Socio-Technical Gap.

From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Rethinking Model Evaluation as Narrowing the Socio-Technical Gap

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Vera Liao, Daniel Gruen, and Sarah Miller

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs S elf C heck GPT : Zero-resource black-box hallucination detection for generative large language models

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Mathewson, Jaylen Pittman, and Richard Evans

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Wolf, Josh Andres, Michael Desmond, Narendra Nath Joshi, Zahra Ashktorab, Aabhas Sharma, Kristina Brimijoin, Qian Pan, Evelyn Duesterwald, and Casey Dugan

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Unresolved cited work

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Rodriguez Mendez, Thang Bui, Alyssa Goodman, Alberto Accomazzi, Jill Naiman, Jesse Cranney, Kevin Schawinski, and Roberta Raileanu

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs emr QA : A large corpus for question answering on electronic medical records

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs B io R ead: A new dataset for biomedical reading comprehension

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs B io MRC : A dataset for biomedical machine reading comprehension

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Exploring temperature effects on large language models across various clinical tasks

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Smith, Huiling Liu, Kevin Schawinski, Kartheik Iyer, Ioana Ciucă, and UniverseTBD

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Bender, Alex Hanna, and Amandalynne Paullada

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs The effect of sampling temperature on problem solving in large language models

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Improving evidence retrieval for automated explainable fact-checking

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs The Good, The Bad, and The Greedy: Evaluation of LLMs Should Not Ignore Non-Determinism

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Scieval: a multi-level large language model evaluation benchmark for scientific research

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Nestor, Ali Soroush, Pierre A

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs FEVER : a large-scale dataset for fact extraction and VER ification

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Group chat ecology in enterprise instant messaging: How employees collaborate through multi-user chat channels on slack

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Evaluating large language models on academic literature understanding and review: An empirical study among early-stage scholars

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs as an ai language model, i cannot

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Designing an Evaluation Framework for Large Language Models in Astronomy Research

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs C-pack: Packed resources for general chinese embeddings

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs AI as an active writer: Interaction strategies with generated text in human- AI collaborative fiction writing

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs User-Controlled Knowledge Fusion in Large Language Models: Balancing Creativity and Hallucination

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Weinberger, and Yoav Artzi

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs WildChat : 1 M C hat GPT interaction logs in the wild

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Deconstructing NLG evaluation: Evaluation practices, assumptions, and their implications

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Navigating the grey area: How expressions of uncertainty and overconfidence affect language models

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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs Relying on the unreliable: The impact of language models ' reluctance to express uncertainty

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