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Robert West

34 accepted papers

2026

Interactive Evaluation of Large Language Models for Multi-Requirement Software Engineering Tasks

AAAI 2026technical

Standard single-turn, static benchmarks fall short in evaluating the nuanced capabilities of Large Language Models (LLMs) on complex tasks such as software engineering. In this work, we propose a novel interactive evaluation framework that assesses LLMs on multi-requirement programming tasks through

Cited by 0SourcePDFScholar
2026

Narrow Finetuning Leaves Clearly Readable Traces in the Activation Differences

ICLR 2026poster

Finetuning on narrow domains has become an essential tool to adapt Large Language Models (LLMs) to specific tasks and to create models with known unusual properties that are useful for safety research. Model diffing--the study of differences between base and finetuned models--is a promising approach…

Cited by 0SourcecodeScholar
2026

RL for Reasoning by Adaptively Revealing Rationales

ICLR 2026poster

Learning in the combinatorially large output space of sequence generation problems is challenging as providing expert demonstrations scales poorly with sequence length, and RL struggles with sparse rewards. Between dense demonstrations in supervised training and no demonstrations in reinforcement l…

Cited by 0SourceScholar
2026

SynthWorlds: Controlled Parallel Worlds for Disentangling Reasoning and Knowledge in Language Models

ICLR 2026poster

Evaluating the reasoning ability of language models (LMs) is complicated by their extensive parametric world knowledge, where benchmark performance often reflects factual recall rather than genuine reasoning. Existing datasets and approaches (e.g., temporal filtering, paraphrasing, adversarial subst…

Cited by 0SourcecodeScholar
2025

A Logical Fallacy-Informed Framework for Argument Generation

NAACL 2025long

Despite the remarkable performance of large language models (LLMs), they still struggle with generating logically sound arguments, resulting in potential risks such as spreading misinformation. An important factor contributing to LLMs’ suboptimal performance in generating coherent arguments is their…

2025

Combining Constrained and Unconstrained Decoding via Boosting: BoostCD and Its Application to Information Extraction

EMNLP 2025

Many recent approaches to structured NLP tasks use an autoregressive language model M to map unstructured input text x to output text y representing structured objects (such as tuples, lists, trees, code, etc.), where the desired output structure is enforced via constrained decoding. During training

Cited by 0SourcePDFScholar
2025

Controllable Context Sensitivity and the Knob Behind It

ICLR 2025poster

When making predictions, a language model must trade off how much it relies on its context vs. its prior knowledge. Choosing how sensitive the model is to its context is a fundamental functionality, as it enables the model to excel at tasks like retrieval-augmented generation and question-answering.…

2025

Fleet of Agents: Coordinated Problem Solving with Large Language Models

ICML 2025poster

While numerous frameworks have been developed to enhance the reasoning abilities of large language models (LLMs), there is a scarcity of methods that effectively balance the trade-off between cost and quality. In this paper, we introduce Fleet of Agents (FoA), a novel and intuitive yet principled f…

2025

GRAD: Generative Retrieval-Aligned Demonstration Sampler for Efficient Few-Shot Reasoning

EMNLP 2025

Large Language Models (LLMs) achieve strong performance across diverse tasks, but their effectiveness often depends on the quality of the provided context. Retrieval-Augmented Generation (RAG) enriches prompts with external information, but its reliance on static databases constrains adaptability an

2025

One-Step is Enough: Sparse Autoencoders for Text-to-Image Diffusion Models

NeurIPS 2025poster

For large language models (LLMs), sparse autoencoders (SAEs) have been shown to decompose intermediate representations that often are not interpretable directly into sparse sums of interpretable features, facilitating better control and subsequent analysis. However, similar analyses and approaches h…

Cited by 0SourceScholar
2025

Separating Tongue from Thought: Activation Patching Reveals Language-Agnostic Concept Representations in Transformers

ACL 2025long

A central question in multilingual language modeling is whether large language models (LLMs) develop a universal concept representation, disentangled from specific languages. In this paper, we address this question by analyzing latent representations (latents) during a word-translation task in trans…

2025

zip2zip: Inference-Time Adaptive Tokenization via Online Compression

NeurIPS 2025poster

Tokenization efficiency plays a critical role in the performance and cost of large language models (LLMs), yet most models rely on static tokenizers optimized on general-purpose corpora. These tokenizers’ fixed vocabularies often fail to adapt to domain- or language-specific inputs, leading to longe…

Cited by 0SourcecodeScholar
2024

A Glitch in the Matrix? Locating and Detecting Language Model Grounding with Fakepedia

ACL 2024long

Large language models (LLMs) have an impressive ability to draw on novel information supplied in their context. Yet the mechanisms underlying this contextual grounding remain unknown, especially in situations where contextual information contradicts factual knowledge stored in the parameters, which…

2024

Activation Scaling for Steering and Interpreting Language Models

EMNLP 2024finding

Given the prompt “Rome is in”, can we steer a language model to flip its prediction of an incorrect token “France” to a correct token “Italy” by only multiplying a few relevant activation vectors with scalars? We argue that successfully intervening on a model is a prerequisite for interpreting its i…

2024

Can Language Models Recognize Convincing Arguments?

EMNLP 2024finding

The capabilities of large language models (LLMs) have raised concerns about their potential to create and propagate convincing narratives. Here, we study their performance in detecting convincing arguments to gain insights into LLMs’ persuasive capabilities without directly engaging in experimentati…

Cited by 10SourcePDFScholar
2024

Do Llamas Work in English? On the Latent Language of Multilingual Transformers

ACL 2024long

We ask whether multilingual language models trained on unbalanced, English-dominated corpora use English as an internal pivot language—-a question of key importance for understanding how language models function and the origins of linguistic bias. Focusing on the Llama-2 family of transformer models…

2024

Entity Insertion in Multilingual Linked Corpora: The Case of Wikipedia

EMNLP 2024main

Links are a fundamental part of information networks, turning isolated pieces of knowledge into a network of information that is much richer than the sum of its parts. However, adding a new link to the network is not trivial: it requires not only the identification of a suitable pair of source and t…

2024

Evaluating Language Model Agency Through Negotiations

ICLR 2024poster

We introduce an approach to evaluate language model (LM) agency using negotiation games. This approach better reflects real-world use cases and addresses some of the shortcomings of alternative LM benchmarks. Negotiation games enable us to study multi-turn, and cross-model interactions, modulate com…

2024

Getting Serious about Humor: Crafting Humor Datasets with Unfunny Large Language Models

ACL 2024short

Humor is a fundamental facet of human cognition and interaction. Yet, despite recent advances in natural language processing, humor detection remains a challenging task that is complicated by the scarcity of datasets that pair humorous texts with similar non-humorous counterparts. We investigate whe…

2024

Making Reasoning Matter: Measuring and Improving Faithfulness of Chain-of-Thought Reasoning

EMNLP 2024finding

Large language models (LLMs) have been shown to perform better when asked to reason step-by-step before answering a question. However, it is unclear to what degree the model’s final answer is faithful to the stated reasoning steps. In this paper, we perform a causal mediation analysis on twelve LLMs…

Cited by 19SourcePDFScholar
2024

Self-Recognition in Language Models

EMNLP 2024finding

A rapidly growing number of applications rely on a small set of closed-source language models (LMs). This dependency might introduce novel security risks if LMs develop self-recognition capabilities. Inspired by human identity verification methods, we propose a novel approach for assessing self-reco…

2024

Sketch-Guided Constrained Decoding for Boosting Blackbox Large Language Models without Logit Access

ACL 2024short

Constrained decoding, a technique for enforcing constraints on language model outputs, offers a way to control text generation without retraining or architectural modifications. Its application is, however, typically restricted to models that give users access to next-token distributions (usually vi…

2023

An Ordinal Latent Variable Model of Conflict Intensity

ACL 2023long

Measuring the intensity of events is crucial for monitoring and tracking armed conflict. Advances in automated event extraction have yielded massive data sets of “who did what to whom” micro-records that enable data-driven approaches to monitoring conflict. The Goldstein scale is a widely-used exper…

2023

Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction

EMNLP 2023long main

Large language models (LLMs) have great potential for synthetic data generation. This work shows that useful data can be synthetically generated even for tasks that cannot be solved directly by LLMs: for problems with structured outputs, it is possible to prompt an LLM to perform the task in the rev…

Cited by 0SourcecodeScholar
2023

Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning

EMNLP 2023long main

Despite their impressive performance, large language models (LMs) still struggle with reliably generating complex output structures when not finetuned to follow the required output format exactly. To address this issue, grammar-constrained decoding (GCD) can be used to control the generation of LMs,…

Cited by 0SourcecodeScholar
2022

GenIE: Generative Information Extraction

NAACL 2022long

Structured and grounded representation of text is typically formalized by closed information extraction, the problem of extracting an exhaustive set of (subject, relation, object) triplets that are consistent with a predefined set of entities and relations from a knowledge base schema. Most existing…

2022

Invariant Language Modeling

EMNLP 2022main

Modern pretrained language models are critical components of NLP pipelines. Yet, they suffer from spurious correlations, poor out-of-domain generalization, and biases.Inspired by recent progress in causal machine learning, in particular the invariant risk minimization (IRM) paradigm, we propose inva…

2021

Classifying Dyads for Militarized Conflict Analysis

EMNLP 2021main

Understanding the origins of militarized conflict is a complex, yet important undertaking. Existing research seeks to build this understanding by considering bi-lateral relationships between entity pairs (dyadic causes) and multi-lateral relationships among multiple entities (systemic causes). The a…

2021

Laughing Heads: Can Transformers Detect What Makes a Sentence Funny?

IJCAI 2021poster

The automatic detection of humor poses a grand challenge for natural language processing. Transformer-based systems have recently achieved remarkable results on this task, but they usually (1) were evaluated in setups where serious vs humorous texts came from entirely different sources, and (2) f…

2021

Node Attribute Completion in Knowledge Graphs with Multi-Relational Propagation

ICASSP 2021accepted

The existing literature on knowledge graph completion mostly focuses on the link prediction task. However, knowledge graphs have an additional incompleteness problem: their nodes possess numerical attributes, whose values are often missing. Our approach, denoted as MRAP, imputes the values of missin…

Cited by 0SourceScholar
2021

Recursive Input and State Estimation: a General Framework for Learning from Time Series With Missing Data

ICASSP 2021accepted

Time series with missing data are signals encountered in important settings for machine learning. Some of the most successful prior approaches for modeling such time series are based on recurrent neural networks that transform the input and previous state to account for the missing observations, and…

Cited by 0SourceScholar