Papers by Alessandro Stolfo
Probing for Arithmetic Errors in Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Using a controlled setting of 3-digit addition, we show that simple probes can accurately decode both the model’s output and the correct answer from hidden states. |
| Approach: | They extend their analysis to structured chain-of-thought traces on addition-only GSM8K problems and find that probes trained on simple arithmetic generalize well to this more complex setting, revealing consistent internal representations. |
| Outcome: | The proposed probes can predict model correctness with over 90% accuracy on addition-only GSM8K problems and guide selective re-prompting of erroneous reasoning steps with minimal disruption to correct outputs. |
A Mechanistic Interpretation of Arithmetic Reasoning in Language Models using Causal Mediation Analysis (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on how large language models process and store information related to arithmetic tasks have shown their behavior inconsistent and context-dependent. |
| Approach: | They propose to mechanize the processing of arithmetic queries by a causal mediation framework. |
| Outcome: | The proposed model improves the performance of arithmetic queries with a set of MLP modules. |
Longtonotes: OntoNotes with Longer Coreference Chains (2023.findings-eacl)
Copied to clipboard
Kumar Shridhar, Nicholas Monath, Raghuveer Thirukovalluru, Alessandro Stolfo, Manzil Zaheer, Andrew McCallum, Mrinmaya Sachan
| Challenge: | Using Ontonotes, documents in certain genres were split into smaller parts for ease of annotation. |
| Approach: | They propose to merge annotations from documents split into smaller parts in Ontonotes for ease of annotation. |
| Outcome: | The proposed corpus restores documents to their original form, revealing dramatic increases in length in certain genres. |
Distilling Reasoning Capabilities into Smaller Language Models (2023.findings-acl)
Copied to clipboard
| Challenge: | a step-by-step reasoning approach like chain of thought has proved to be effective in eliciting reasoning abilities in large language models. |
| Approach: | They propose a knowledge distillation approach that leverages CoT reasoning capabilities of larger models and distills them into smaller models. |
| Outcome: | The proposed scheme boosts the performance of smaller models over 70% on multiple reasoning datasets. |
A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Recent work shows that language models can rely on shallow patterns in problem description when generating a solution. |
| Approach: | They propose a framework which pins down the causal effect of various factors on the output solution. |
| Outcome: | The proposed framework improves robustness and sensitivity to direct interventions on a test bed of math word problems. |
Groundedness in Retrieval-augmented Long-form Generation: An Empirical Study (2024.findings-naacl)
Copied to clipboard
| Challenge: | a significant portion of correct answers remain compromised by hallucinations in large language models. |
| Approach: | They examine whether every generated sentence is grounded in retrieved documents or the model’s pre-training data. |
| Outcome: | The findings highlight the need for more robust mechanisms in large language models to mitigate the generation of ungrounded content. |
A Simple Unsupervised Approach for Coreference Resolution using Rule-based Weak Supervision (2022.starsem-1)
Copied to clipboard
| Challenge: | state-of-the-art coreference models rely on labeled data, but an end-to-end model is needed to solve this problem. |
| Approach: | They propose an approach that leverages an end-to-end neural model in settings where labeled data is unavailable. |
| Outcome: | The proposed approach outperforms the previous best unsupervised model and outperformed the rule-based model on English OntoNotes corpus. |
Towards a Mechanistic Interpretation of Multi-Step Reasoning Capabilities of Language Models (2023.emnlp-main)
Copied to clipboard
Yifan Hou, Jiaoda Li, Yu Fei, Alessandro Stolfo, Wangchunshu Zhou, Guangtao Zeng, Antoine Bosselut, Mrinmaya Sachan
| Challenge: | Recent work has shown that language models (LMs) have strong multi-step (i.e., procedural) reasoning capabilities. |
| Approach: | They propose a mechanistic interpretation of language models for multi-step reasoning tasks by introducing a new probing approach that recovers the reasoning tree from the model’s attention patterns. |
| Outcome: | The proposed model implicitly embeds a reasoning tree resembling the correct reasoning process within it, and detects the information from the model’s attention patterns for most examples. |