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Publications
PREFERENCE OPTIMIZATION FOR CONCEPT BOTTLENECK MODELS
Concept Bottleneck Models (CBMs) propose to enhance the trustworthiness of
AI systems by constraining their decisions on a set of human-unde… (voir plus)rstandable
concepts. However, CBMs typically assume that datasets contain accurate concept
labels—an assumption often violated in practice, which we show can significantly
degrade performance (by 25% in some cases). To address this, we introduce the
Concept Preference Optimization (CPO) objective, a new loss function based on
Direct Preference Optimization, which effectively mitigates the negative impact
of concept mislabeling on CBM performance. We provide an analysis of some
key properties of the CPO objective showing it directly optimizes for the concept’s
posterior distribution, and contrast it against Binary Cross Entropy (BCE) where
we show CPO is inherently less sensitive to concept noise. We empirically confirm
our analysis finding that CPO consistently outperforms BCE in three real-world
datasets with and without added label noise.
Large Language Models (LLMs) have exhibited an impressive capability to perform reasoning tasks, especially if they are encouraged to genera… (voir plus)te a sequence of intermediate steps. Reasoning performance can be improved by suitably combining multiple LLM responses, generated either in parallel in a single query, or via sequential interactions with LLMs throughout the reasoning process. Existing strategies for combination, such as self-consistency and progressive-hint-prompting, make inefficient usage of the LLM responses. We present Refined Answer Distributions, a novel and principled algorithmic framework to enhance the reasoning capabilities of LLMs. Our approach can be viewed as an iterative sampling strategy for forming a Monte Carlo approximation of an underlying distribution of answers, with the goal of identifying the mode --- the most likely answer. Empirical evaluation on several reasoning benchmarks demonstrates the superiority of the proposed approach.
This paper takes a position on how anti-misinformation AI works should be developed for the online misinformation context. We observe that t… (voir plus)he current literature is dominated by works that produce more information for users to process and that this function faces various challenges in bringing meaningful effects to reality. We use anti-misinformation insights from other domains to suggest a redirection of the existing line of work and identify an under-explored opportunity AI can facilitate exploring.
This paper takes a position on how anti-misinformation AI works should be developed for the online misinformation context. We observe that t… (voir plus)he current literature is dominated by works that produce more information for users to process and that this function faces various challenges in bringing meaningful effects to reality. We use anti-misinformation insights from other domains to suggest a redirection of the existing line of work and identify an under-explored opportunity AI can facilitate exploring.
Large language models (LLMs) are known to "hallucinate" by generating false or misleading outputs. Hallucinations pose various harms, from e… (voir plus)rosion of trust to widespread misinformation. Existing hallucination evaluation, however, focuses only on "correctness" and often overlooks "consistency", necessary to distinguish and address these harms. To bridge this gap, we introduce _prompt multiplicity_, a framework for quantifying consistency through prompt sensitivity. Our analysis reveals significant multiplicity (over 50% inconsistency in benchmarks like Med-HALT), suggesting that hallucination-related harms have been severely underestimated. Furthermore, we study the role of consistency in hallucination detection and mitigation. We find that: (a) detection techniques capture consistency, not correctness, and (b) mitigation techniques like RAG can introduce additional inconsistencies. By integrating prompt multiplicity into hallucination evaluation, we provide an improved framework of potential harms and uncover critical limitations in current detection and mitigation strategies.
Transition path sampling (TPS) is an important method for studying rare events, such as they happen in chemical reactions or protein folding… (voir plus). These events occur so infrequently that traditional simulations are often impractical, and even recent machine-learning approaches struggle to address this issue for larger systems. In this paper, we propose using modern deep learning techniques to improve the scalability of TPS methods significantly. We highlight the need for better evaluations in the existing literature and start by formulating TPS as a sampling problem over an unnormalized target density and introduce relevant evaluation metrics to assess the effectiveness of TPS solutions from this perspective. To develop a scalable approach, we explore several design choices, including a problem-informed neural network architecture, simulated annealing, the integration of prior knowledge into the sampling process, and attention mechanisms. Finally, we conduct a comprehensive empirical study and compare these design choices with other recently developed deep-learning methods for rare event sampling.
Recent progress in large language models (LLMs) has focused on producing responses that meet human expectations and align with shared values… (voir plus) - a process coined alignment. However, aligning LLMs remains challenging due to the inherent disconnect between the complexity of human values and the narrow nature of the technological approaches designed to address them. Current alignment methods often lead to misspecified objectives, reflecting the broader issue of incomplete contracts, the impracticality of specifying a contract between a model developer, and the model that accounts for every scenario in LLM alignment. In this paper, we argue that improving LLM alignment requires incorporating insights from societal alignment frameworks, including social, economic, and contractual alignment, and discuss potential solutions drawn from these domains. Given the role of uncertainty within societal alignment frameworks, we then investigate how it manifests in LLM alignment. We end our discussion by offering an alternative view on LLM alignment, framing the underspecified nature of its objectives as an opportunity rather than perfect their specification. Beyond technical improvements in LLM alignment, we discuss the need for participatory alignment interface designs.
Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many scientifi… (voir plus)c disciplines. However, its real-world applications remain limited. Current methods often rely on unrealistic assumptions and are evaluated only on simple synthetic toy datasets, often with inadequate evaluation metrics. In this paper, we substantiate these claims by performing a systematic review of the recent causal discovery literature. We present applications in biology, neuroscience, and Earth sciences - fields where causal discovery holds promise for addressing key challenges. We highlight available simulated and real-world datasets from these domains and discuss common assumption violations that have spurred the development of new methods. Our goal is to encourage the community to adopt better evaluation practices by utilizing realistic datasets and more adequate metrics.
Large language models (LLMs) are known to "hallucinate" by generating false or misleading outputs. Existing hallucination benchmarks often o… (voir plus)verlook prompt sensitivity, due to stable accuracy scores despite prompt variations. However, such stability can be misleading. In this work, we introduce prompt multiplicity--the multiplicity of individual hallucinations depending on the input prompt--and study its role in LLM hallucination benchmarks. We find severe multiplicity, with even more than 50% of responses changing between correct and incorrect answers simply based on the prompt for certain benchmarks, like Med-HALT. Prompt multiplicity also gives us the lens to distinguish between randomness in generation and consistent factual inaccuracies, providing a more nuanced understanding of LLM hallucinations and their real-world harms. By situating our discussion within existing hallucination taxonomies--supporting their quantification--and exploring its relationship with uncertainty in generation, we highlight how prompt multiplicity fills a critical gap in the literature on LLM hallucinations.
Dynamically integrating new or rapidly evolving information after Language Model (LM) pre-training remains challenging, particularly in low-… (voir plus)data scenarios or when dealing with private and specialized documents. In-context learning and retrieval-augmented generation (RAG) face limitations, including their high inference costs and their inability to capture global document information. In this paper, we propose a way of modularizing knowledge by training Knowledge Modules (KMs). KMs are lightweight components implemented as parameter-efficient LoRA modules, which are trained to store information about new documents and can be easily plugged into models on demand. We show that next-token prediction performs poorly in training KMs. We instead propose Deep Context Distillation: we learn KMs parameters such as to simulate hidden states and logits of a teacher that takes the document in context. Our method outperforms standard next-token prediction and pre-instruction training techniques, across two datasets. Finally, we highlight synergies between KMs and retrieval-augmented generation.
Type inference for dynamic languages like Python is a persistent challenge in software engineering. While large language models (LLMs) have … (voir plus)shown promise in code understanding, their type inference capabilities remain underexplored. We introduce `TypyBench`, a benchmark designed to evaluate LLMs' type inference across entire Python repositories. `TypyBench` features two novel metrics: `TypeSim`, which captures nuanced semantic relationships between predicted and ground truth types, and `TypeCheck`, which assesses type consistency across codebases. Our evaluation of various LLMs on a curated dataset of 50 high-quality Python repositories reveals that, although LLMs achieve decent `TypeSim` scores, they struggle with complex nested types and exhibit significant type consistency errors. These findings suggest that future research should shift focus from improving type similarity to addressing repository-level consistency. `TypyBench` provides a foundation for this new direction, offering insights into model performance across different type complexities and usage contexts.
