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Found 21 papers, 17 papers with code
OLMoE: Open Mixture-of-Experts Language Models
We introduce OLMoE, a fully open, state-of-the-art language model leveraging sparse Mixture-of-Experts (MoE).
CHIME: LLM-Assisted Hierarchical Organization of Scientific Studies for Literature Review Support
In this work, we investigate the potential of LLMs for producing hierarchical organizations of scientific studies to assist researchers with literature review.
SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature
We present SciRIFF (Scientific Resource for Instruction-Following and Finetuning), a dataset of 137K instruction-following demonstrations for 54 tasks covering five essential scientific literature understanding capabilities: information extraction, summarization, question answering, claim verification, and classification.
Source-Aware Training Enables Knowledge Attribution in Language Models
We investigate the problem of intrinsic source citation, where LLMs are required to cite the pretraining source supporting a generated response.
KIWI: A Dataset of Knowledge-Intensive Writing Instructions for Answering Research Questions
To evaluate the capabilities of current LLMs on this task, we construct KIWI, a dataset of knowledge-intensive writing instructions in the scientific domain.
Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2
Since the release of T\"ULU [Wang et al., 2023b], open resources for instruction tuning have developed quickly, from better base models to new finetuning techniques.
Language Models Hallucinate, but May Excel at Fact Verification
Recent progress in natural language processing (NLP) owes much to remarkable advances in large language models (LLMs).
When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets
Using large language models (LMs) for query or document expansion can improve generalization in information retrieval.
Estimating the Causal Effect of Early ArXiving on Paper Acceptance
However, since quality is a challenging construct to estimate, we use the negative outcome control method, using paper citation count as a control variable to debias the quality confounding effect.
How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources
Our evaluations show that the best model in any given evaluation reaches on average 87% of ChatGPT performance, and 73% of GPT-4 performance, suggesting that further investment in building better base models and instruction-tuning data is required to close the gap.
Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction
In this paper, we conduct a human-centered study of how language models may assist people in reframing negative thoughts.
SciFact-Open: Towards open-domain scientific claim verification
While research on scientific claim verification has led to the development of powerful systems that appear to approach human performance, these approaches have yet to be tested in a realistic setting against large corpora of scientific literature.
Generating Scientific Claims for Zero-Shot Scientific Fact Checking
To address this challenge, we propose scientific claim generation, the task of generating one or more atomic and verifiable claims from scientific sentences, and demonstrate its usefulness in zero-shot fact checking for biomedical claims.
MultiVerS: Improving scientific claim verification with weak supervision and full-document context
Our approach outperforms two competitive baselines on three scientific claim verification datasets, with particularly strong performance in zero / few-shot domain adaptation experiments.
Overview and Insights from the SciVer Shared Task on Scientific Claim Verification
We present an overview of the SciVer shared task, presented at the 2nd Scholarly Document Processing (SDP) workshop at NAACL 2021.
Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study
Biomedical knowledge graphs (KGs) hold rich information on entities such as diseases, drugs, and genes.
Extracting a Knowledge Base of Mechanisms from COVID-19 Papers
The COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge.
The Effect of Moderation on Online Mental Health Conversations
We found that participation in group mental health discussions led to improvements in psychological perspective, and that these improvements were larger in moderated conversations.
Fact or Fiction: Verifying Scientific Claims
We introduce scientific claim verification, a new task to select abstracts from the research literature containing evidence that SUPPORTS or REFUTES a given scientific claim, and to identify rationales justifying each decision.
Entity, Relation, and Event Extraction with Contextualized Span Representations
We examine the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction.
Ranked #6 on
Joint Entity and Relation Extraction
on SciERC
