26 citations · 73 across the 21 of their papers we have counts for
7 papers · 1 filter
From Models to Microtheories: Distilling a Model's Topical Knowledge for Grounded Question Answering
Nathaniel Weir, Bhavana Dalvi Mishra, Orion Weller +6
Recent reasoning methods (e.g., chain-of-thought, entailment reasoning) help users understand how language models (LMs) answer a single question, but they do little to reveal the L…
Core: Robust Factual Precision with Informative Sub-Claim Identification
Zhengping Jiang, Jingyu Zhang, Nathaniel Weir +6
Hallucinations pose a challenge to the application of large language models (LLMs) thereby motivating the development of metrics to evaluate factual precision. We observe that popu…
Learning to Reason via Program Generation, Emulation, and Search
Nathaniel Weir, Muhammad Khalifa, Linlu Qiu +2
Program synthesis with language models (LMs) has unlocked a large set of reasoning abilities; code-tuned LMs have proven adept at generating programs that solve a wide variety of a…
SELF-[IN]CORRECT: LLMs Struggle with Discriminating Self-Generated Responses
Dongwei Jiang, Jingyu Zhang, Orion Weller +3
Can LLMs consistently improve their previous outputs for better results? For this to be true, LLMs would need to be better at discriminating among previously-generated alternatives…
TV-TREES: Multimodal Entailment Trees for Neuro-Symbolic Video Reasoning
Kate Sanders, Nathaniel Weir, Benjamin Van Durme
It is challenging for models to understand complex, multimodal content such as television clips, and this is in part because video-language models often rely on single-modality rea…
Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic
Nathaniel Weir, Kate Sanders, Orion Weller +8
Recent language models enable new opportunities for structured reasoning with text, such as the construction of intuitive, proof-like textual entailment trees without relying on br…