3 citations · 25 across the 55 of their papers we have counts for
19 papers · 1 filter
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…
System-1.x: Learning to Balance Fast and Slow Planning with Language Models
Swarnadeep Saha, Archiki Prasad, Justin Chih-Yao Chen +3
Language models can be used to solve long-horizon planning problems in two distinct modes: a fast 'System-1' mode, directly generating plans without any explicit search or backtrac…
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…
Ctrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse Controls to Any Diffusion Model
Han Lin, Jaemin Cho, Abhay Zala +1
ControlNets are widely used for adding spatial control to text-to-image diffusion models with different conditions, such as depth maps, scribbles/sketches, and human poses. However…
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…
Rethinking Interactive Image Segmentation with Low Latency, High Quality, and Diverse Prompts
Qin Liu, Jaemin Cho, Mohit Bansal +1
The goal of interactive image segmentation is to delineate specific regions within an image via visual or language prompts. Low-latency and high-quality interactive segmentation wi…
Contrastive Region Guidance: Improving Grounding in Vision-Language Models without Training
David Wan, Jaemin Cho, Elias Stengel-Eskin +1
Highlighting particularly relevant regions of an image can improve the performance of vision-language models (VLMs) on various vision-language (VL) tasks by guiding the model to at…
VLN-Video: Utilizing Driving Videos for Outdoor Vision-and-Language Navigation
Jialu Li, Aishwarya Padmakumar, Gaurav Sukhatme +1
Outdoor Vision-and-Language Navigation (VLN) requires an agent to navigate through realistic 3D outdoor environments based on natural language instructions. The performance of exis…
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…
ReGAL: Refactoring Programs to Discover Generalizable Abstractions
Elias Stengel-Eskin, Archiki Prasad, Mohit Bansal
While large language models (LLMs) are increasingly being used for program synthesis, they lack the global view needed to develop useful abstractions; they generally predict progra…