Papers by James Fan
FOLIO: Natural Language Reasoning with First-Order Logic (2024.emnlp-main)
Copied to clipboard
Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Wenfei Zhou, James Coady, David Peng, Yujie Qiao, Luke Benson, Lucy Sun, Alexander Wardle-Solano, Hannah Szabó, Ekaterina Zubova, Matthew Burtell, Jonathan Fan, Yixin Liu, Brian Wong, Malcolm Sailor, Ansong Ni, Linyong Nan, Jungo Kasai, Tao Yu, Rui Zhang, Alexander Fabbri, Wojciech Kryscinski, Semih Yavuz, Ye Liu, Xi Lin, Shafiq Joty, Yingbo Zhou, Caiming Xiong, Rex Ying, Arman Cohan, Dragomir Radev
| Challenge: | Existing benchmarks for logical reasoning in large language models lack language naturalness or limited complexity. |
| Approach: | They propose to use first-order logic annotations to evaluate logical reasoning capabilities of large language models. |
| Outcome: | The proposed dataset evaluates the FOL reasoning ability of supervised fine-tuning on medium-sized language models. |
Alternative Input Signals Ease Transfer in Multilingual Machine Translation (2022.acl-long)
Copied to clipboard
| Challenge: | Recent work in multilingual machine translation (MMT) has focused on the potential of positive transfer between languages. |
| Approach: | They propose to augment training data with alternative signals that unify different writing systems, such as phonetic, romanized, and transliterated input. |
| Outcome: | The proposed model outperforms strong ensemble baselines on Indic and Turkic languages by 1.3 BLEU points on both languages. |
A Large Scale Speech Sentiment Corpus (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing corpus for sentiment analysis uses text inputs, but voice inputs are becoming more important as smart assistants and mobile voice control become more prevalent. |
| Approach: | They propose to extend the Switchboard-1 Telephone Speech Corpus by adding sentiment labels from 3 different human annotators for every transcript segment. |
| Outcome: | The proposed corpus contains 49500 labeled speech segments covering 140 hours of audio. |
RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question (2023.findings-acl)
Copied to clipboard
Alireza Mohammadshahi, Thomas Scialom, Majid Yazdani, Pouya Yanki, Angela Fan, James Henderson, Marzieh Saeidi
| Challenge: | Existing metrics for evaluating the quality of automatically generated questions are expensive and penalise valid questions that may not have high lexical or semantic similarity to the reference questions. |
| Approach: | They propose a question-answering and span scorer metric based on the answerability of the candidate question given the context. |
| Outcome: | The proposed metric has higher correlation with human judgment without relying on the reference question. |
KILT: a Benchmark for Knowledge Intensive Language Tasks (2021.naacl-main)
Copied to clipboard
Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick Lewis, Majid Yazdani, Nicola De Cao, James Thorne, Yacine Jernite, Vladimir Karpukhin, Jean Maillard, Vassilis Plachouras, Tim Rocktäschel, Sebastian Riedel
| Challenge: | Existing models for knowledge-intensive language tasks require access to large, external knowledge sources. |
| Approach: | They propose a benchmark for knowledge-intensive language tasks (KILT) they test a shared dense vector index coupled with a seq2seq model to generate disambiguated text. |
| Outcome: | The proposed model outperforms tailor-made approaches on fact checking, open-domain question answering and dialog by generating disambiguated text. |
End-to-End Optimization for Multimodal Retrieval-Augmented Generation via Reward Backpropagation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | MM-RAG is a promising approach for enhancing the reliability and factuality of large vision-language models . current methods focus on component-level optimizations and necessitate extensive component-specific training datasets . |
| Approach: | They propose a new paradigm that backpropagates global rewards to each component . this backpropage transforms local losses into specific local losses . |
| Outcome: | The proposed paradigm achieves high training efficiency on knowledge-intensive multimodal benchmarks. |
Tricks for Training Sparse Translation Models (2022.naacl-main)
Copied to clipboard
| Challenge: | Multitask learning with an unbalanced data distribution skews model learning towards high resource tasks. |
| Approach: | They propose to use a temperature heating mechanism and dense pre-training to mitigate this by training models with a fixed model capacity. |
| Outcome: | The proposed techniques improve performance on two multilingual translation benchmarks compared to BASELayers and Dense scaling baselines and in combination, more than 2x model convergence speed. |