4 citations · 10 across the 19 of their papers we have counts for
16 papers · 1 filter
Is Inference Mediated by Distinct Semantic Structures in LLMs? A Mechanistic Interpretation
Nura Aljaafari, Marco Valentino, André Freitas
Predicting a label correctly does not necessarily require representing the operation that produces it. Transformer representations are known to carry label-level information, but w…
GeoMathCode: Understanding Interleaved Math-Code Reasoning for Geometry Problem Solving
Yingji Zhang, Yong Dai, André Freitas
Mathematical reasoning is a hallmark of human intelligence, requiring logical deduction, symbolic manipulation, and abstract thinking. Recent multimodal large language models (MLLM…
Inferring Latent Intentions: Attributional Natural Language Inference in LLM Agents
Xin Quan, Jiafeng Xiong, Marco Valentino +1
Attributional inference, the ability to predict latent intentions behind observed actions, is a critical yet underexplored capability for large language models (LLMs) operating in…
Adaptive LLM-Symbolic Reasoning via Dynamic Logical Solver Composition
Lei Xu, Pierre Beckmann, Marco Valentino +1
Neuro-symbolic NLP methods aim to leverage the complementary strengths of large language models and formal logical solvers. However, current approaches are mostly static in nature,…
Faithful and Robust LLM-Driven Theorem Proving for NLI Explanations
Xin Quan, Marco Valentino, Louise A. Dennis +1
Natural language explanations play a fundamental role in Natural Language Inference (NLI) by revealing how premises logically entail hypotheses. Recent work has shown that the inte…
Improving Chain-of-Thought Reasoning via Quasi-Symbolic Abstractions
Leonardo Ranaldi, Marco Valentino, Andrè Freitas
Chain-of-Though (CoT) represents a common strategy for reasoning in Large Language Models (LLMs) by decomposing complex tasks into intermediate inference steps. However, explanatio…
Reasoning Circuits in Language Models: A Mechanistic Interpretation of Syllogistic Inference
Geonhee Kim, Marco Valentino, André Freitas
Recent studies on reasoning in language models (LMs) have sparked a debate on whether they can learn systematic inferential principles or merely exploit superficial patterns in the…
Verification and Refinement of Natural Language Explanations through LLM-Symbolic Theorem Proving
Xin Quan, Marco Valentino, Louise A. Dennis +1
Natural language explanations represent a proxy for evaluating explanation-based and multi-step Natural Language Inference (NLI) models. However, assessing the validity of explanat…
SemEval-2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials
Mael Jullien, Marco Valentino, André Freitas
Large Language Models (LLMs) are at the forefront of NLP achievements but fall short in dealing with shortcut learning, factual inconsistency, and vulnerability to adversarial inpu…
A Differentiable Integer Linear Programming Solver for Explanation-Based Natural Language Inference
Mokanarangan Thayaparan, Marco Valentino, André Freitas
Integer Linear Programming (ILP) has been proposed as a formalism for encoding precise structural and semantic constraints for Natural Language Inference (NLI). However, traditiona…
Estimating the Causal Effects of Natural Logic Features in Transformer-Based NLI Models
Julia Rozanova, Marco Valentino, André Freitas
Rigorous evaluation of the causal effects of semantic features on language model predictions can be hard to achieve for natural language reasoning problems. However, this is such a…
Inference to the Best Explanation in Large Language Models
Dhairya Dalal, Marco Valentino, André Freitas +1
While Large Language Models (LLMs) have found success in real-world applications, their underlying explanatory process is still poorly understood. This paper proposes IBE-Eval, a f…
Enhancing Ethical Explanations of Large Language Models through Iterative Symbolic Refinement
Xin Quan, Marco Valentino, Louise A. Dennis +1
An increasing amount of research in Natural Language Inference (NLI) focuses on the application and evaluation of Large Language Models (LLMs) and their reasoning capabilities. Des…
Improving Semantic Control in Discrete Latent Spaces with Transformer Quantized Variational Autoencoders
Yingji Zhang, Danilo S. Carvalho, Marco Valentino +2
Achieving precise semantic control over the latent spaces of Variational AutoEncoders (VAEs) holds significant value for downstream tasks in NLP as the underlying generative mechan…
LlaMaVAE: Guiding Large Language Model Generation via Continuous Latent Sentence Spaces
Yingji Zhang, Danilo S. Carvalho, Ian Pratt-Hartmann +1
Deep generative neural networks, such as Variational AutoEncoders (VAEs), offer an opportunity to better understand and control language models from the perspective of sentence-lev…
Graph-Induced Syntactic-Semantic Spaces in Transformer-Based Variational AutoEncoders
Yingji Zhang, Marco Valentino, Danilo S. Carvalho +2
The injection of syntactic information in Variational AutoEncoders (VAEs) has been shown to result in an overall improvement of performances and generalisation. An effective strate…