3 citations · 25 across the 55 of their papers we have counts for
13 papers · 2 filters
Self-Consistency Preference Optimization
Archiki Prasad, Weizhe Yuan, Richard Yuanzhe Pang +6
Self-alignment, whereby models learn to improve themselves without human annotation, is a rapidly growing research area. However, existing techniques often fail to improve complex…
Teaching Models to Balance Resisting and Accepting Persuasion
Elias Stengel-Eskin, Peter Hase, Mohit Bansal
Large language models (LLMs) are susceptible to persuasion, which can pose risks when models are faced with an adversarial interlocutor. We take a first step towards defending mode…
DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback
Zaid Khan, Elias Stengel-Eskin, Jaemin Cho +1
The process of creating training data to teach models is currently driven by humans, who manually analyze model weaknesses and plan how to create data that improves a student model…
LASeR: Learning to Adaptively Select Reward Models with Multi-Armed Bandits
Duy Nguyen, Archiki Prasad, Elias Stengel-Eskin +1
Reward Models (RMs) are crucial to aligning large language models (LLMs), but the degree to which an RM specialized to one task (e.g. writing) generalizes to new tasks (e.g. math)…
MAgICoRe: Multi-Agent, Iterative, Coarse-to-Fine Refinement for Reasoning
Justin Chih-Yao Chen, Archiki Prasad, Swarnadeep Saha +2
Large Language Models' (LLM) reasoning can be improved using test-time aggregation strategies, i.e., generating multiple samples and voting among generated samples. While these imp…
AdaCAD: Adaptively Decoding to Balance Conflicts between Contextual and Parametric Knowledge
Han Wang, Archiki Prasad, Elias Stengel-Eskin +1
Knowledge conflict arises from discrepancies between information in the context of a large language model (LLM) and the knowledge stored in its parameters. This can hurt performanc…
Fundamental Problems With Model Editing: How Should Rational Belief Revision Work in LLMs?
Peter Hase, Thomas Hofweber, Xiang Zhou +2
The model editing problem concerns how language models should learn new facts about the world over time. While empirical research on model editing has drawn widespread attention, t…
See It from My Perspective: How Language Affects Cultural Bias in Image Understanding
Amith Ananthram, Elias Stengel-Eskin, Mohit Bansal +1
Vision-language models (VLMs) can respond to queries about images in many languages. However, beyond language, culture affects how we see things. For example, individuals from West…
Are language models rational? The case of coherence norms and belief revision
Thomas Hofweber, Peter Hase, Elias Stengel-Eskin +1
Do norms of rationality apply to machine learning models, in particular language models? In this paper we investigate this question by focusing on a special subset of rational norm…
LACIE: Listener-Aware Finetuning for Confidence Calibration in Large Language Models
Elias Stengel-Eskin, Peter Hase, Mohit Bansal
When answering questions, LLMs can convey not only an answer, but a level of confidence about the answer being correct. This includes explicit confidence markers (e.g. giving a num…
Soft Self-Consistency Improves Language Model Agents
Han Wang, Archiki Prasad, Elias Stengel-Eskin +1
Generations from large language models (LLMs) can be improved by sampling and scoring multiple solutions to select a final answer. Current "sample and select" methods such as self-…
GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations
Jinhao Duan, Renming Zhang, James Diffenderfer +6
As Large Language Models (LLMs) are integrated into critical real-world applications, their strategic and logical reasoning abilities are increasingly crucial. This paper evaluates…
MAGDi: Structured Distillation of Multi-Agent Interaction Graphs Improves Reasoning in Smaller Language Models
Justin Chih-Yao Chen, Swarnadeep Saha, Elias Stengel-Eskin +1
Multi-agent interactions between Large Language Model (LLM) agents have shown major improvements on diverse reasoning tasks. However, these involve long generations from multiple m…