3 citations · 4 across the 6 of their papers we have counts for
7 papers
Memory vs. Context? Influential Factors of Factual Recall in Language Models
Guilhem Fouilhé, Nicholas Asher, Philippe Muller
We reproduce and stress-test the work of Yu et al. (2023), who characterize how language models (LMs) arbitrate between memorized knowledge and contradictory in-context statements.…
Exploring Plan Space through Conversation: An Agentic Framework for LLM-Mediated Explanations in Planning
Guilhem Fouilhé, Rebecca Eifler, Antonin Poché +2
When automating plan generation for a real-world sequential decision problem, the goal is often not to replace the human planner, but to facilitate an iterative reasoning and elici…
Interpreto: An Explainability Library for Transformers
Antonin Poché, Thomas Mullor, Gabriele Sarti +8
Interpreto is an open-source Python library for interpreting HuggingFace language models, from early BERT variants to LLMs. It provides two complementary families of methods: attri…
COCORELI: Enforcing Execution Preconditions for Reliable Collaborative Instruction Following
Swarnadeep Bhar, Omar Naim, Eleni Metheniti +4
Autonomous agents executing human instructions must operate reliably even when instructions are incomplete. While recent approaches improve detection of missing information, detect…
SSA: Improving Performance With a Better Scoring Function
Omar Naim, Swarnadeep Bhar, Jérôme Bolte +1
While transformer models exhibit strong in-context learning (ICL) abilities, they often fail to generalize under simple distribution shifts. We analyze these failures and identify…
Strong hallucinations from negation and how to fix them
Nicholas Asher, Swarnadeep Bhar
Despite great performance on many tasks, language models (LMs) still struggle with reasoning, sometimes providing responses that cannot possibly be true because they stem from logi…
Analyzing Semantic Faithfulness of Language Models via Input Intervention on Question Answering
Akshay Chaturvedi, Swarnadeep Bhar, Soumadeep Saha +2
Transformer-based language models have been shown to be highly effective for several NLP tasks. In this paper, we consider three transformer models, BERT, RoBERTa, and XLNet, in bo…