Papers by Fangyu Liu
Answering Numerical Reasoning Questions in Table-Text Hybrid Contents with Graph-based Encoder and Tree-based Decoder (2022.coling-1)
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| Challenge: | Existing methods for numerical reasoning are not flexible enough to handle diverse expressions. |
| Approach: | They propose a Relational Graph enhanced Hybrid table-text Numerical reasoning model with Tree decoder which captures relationship between numerical value, table schema, and text information on the encoder side. |
| Outcome: | The proposed model outperforms the baseline model and achieves state-of-the-art results on the publicly available tabletext hybrid QA benchmark. |
Mixture-of-Partitions: Infusing Large Biomedical Knowledge Graphs into BERT (2021.emnlp-main)
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| Challenge: | Infusing factual knowledge into pre-trained models is fundamental for many knowledge-intensive tasks. |
| Approach: | They propose an infusion approach that partitions a large knowledge graph into smaller sub-graphs and infuses their specific knowledge into various BERT models using lightweight adapters. |
| Outcome: | The proposed approach improves the underlying BERTs and achieves new SOTA performance on six downstream tasks. |
Chart-based Reasoning: Transferring Capabilities from LLMs to VLMs (2024.findings-naacl)
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Victor Carbune, Hassan Mansoor, Fangyu Liu, Rahul Aralikatte, Gilles Baechler, Jindong Chen, Abhanshu Sharma
| Challenge: | Visual language models (VLMs) are achieving increasingly strong performance on multimodal tasks. |
| Approach: | They propose to transfer reasoning capabilities from large-language models to VLMs by constructing a 20x larger dataset and a larger dataset to improve general reasoning capabilities. |
| Outcome: | The proposed model outperforms larger models without an upstream OCR system while keeping inference time constant. |
Probing Cross-Lingual Lexical Knowledge from Multilingual Sentence Encoders (2023.eacl-main)
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| Challenge: | Pretrained multilingual language models (LMs) can be 'rewired' into effective multilingual sentence encoders (SEs) however, it remains unclear how to best leverage them to represent sub-sentence lexical items in cross-lingual lexicals. |
| Approach: | They propose a method for exposing cross-lingual lexical knowledge by additional fine-tuning through inexpensive contrastive learning that requires only a small amount of word translation pairs. |
| Outcome: | The proposed method exposes cross-lingual lexical knowledge by additional fine-tuning through inexpensive contrastive learning that requires only a small amount of word translation pairs. |
MenatQA: A New Dataset for Testing the Temporal Comprehension and Reasoning Abilities of Large Language Models (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) have shown nearly saturated performance on many NLP tasks. |
| Approach: | They construct multiple sensitive factors time QA which encompasses three temporal factors . they test current mainstream LLMs with different parameter sizes . |
| Outcome: | The proposed model incorporates three temporal factors with 2,853 samples . the results show that LLMs fall behind smaller models on these factors . |
Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders (2021.emnlp-main)
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| Challenge: | Existing studies have shown that pretrained Masked Language Models are not effective as universal lexical and sentence encoders off-the-shelf, i.e., without further task-specific fine-tuning on NLI, sentence similarity, or paraphrasing tasks using annotated task data. |
| Approach: | They propose a contrastive learning technique which turns pretrained MLMs into effective universal lexical and sentence encoders without additional data. |
| Outcome: | The proposed technique can turn MLMs into effective universal lexical and sentence encoders even without additional data. |
LUQ: Long-text Uncertainty Quantification for LLMs (2024.emnlp-main)
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| Challenge: | Existing research on Uncertainty Quantification (UQ) predominantly targets short text generation, however, real-world applications often necessitate much longer responses. |
| Approach: | They propose a method that ensembles responses from multiple models and selects the response with the lowest uncertainty. |
| Outcome: | The proposed method outperforms baseline methods in correlating with the model’s factuality scores (negative coefficient of -0.85 observed for Gemini Pro). |
Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking (2021.acl-short)
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| Challenge: | Existing work on transferring domain-specific knowledge from a pretraining model to a resource-poor language is limited to English . a novel cross-lingual biomedical entity linking task is proposed to improve this capability. |
| Approach: | They propose a cross-lingual biomedical entity linking task and establish a new benchmark spanning 10 typologically diverse languages. |
| Outcome: | The proposed methods yield consistent gains across all target languages, sometimes up to 20 Precision@1 points, without any in-domain knowledge in the target language and without any parallel data. |
MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering (2023.acl-long)
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Fangyu Liu, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Yasemin Altun, Nigel Collier, Julian Eisenschlos
| Challenge: | Visual language models that are pretraining on natural images or image-text pairs crawled from the web perform poorly on visual language tasks such as ChartQA and ChartQA. |
| Approach: | They propose to perform several pretraining tasks that cover plot deconstruction and numerical reasoning which are key capabilities in visual language modeling. |
| Outcome: | The proposed model outperforms state-of-the-art methods on benchmarks such as PlotQA and ChartQA by as much as 20%. |
WinoDict: Probing language models for in-context word acquisition (2023.eacl-main)
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| Challenge: | Large Language Models (LLMs) are unable to reflect the way language changes over time as their training corpus is frozen in time. |
| Approach: | They propose a new in-context learning paradigm to measure Large Language Models' ability to learn novel words during inference. |
| Outcome: | The proposed model improves on Winograd-style co-reference resolution problems by replacing the key concept word with a plausible word that the model must understand to complete the task. |
Visually Grounded Reasoning across Languages and Cultures (2021.emnlp-main)
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| Challenge: | a new protocol allows for a multilingual hierarchy of concepts and images based on native speakers . the results suggest that the current models are not robust enough to handle multilingual data . |
| Approach: | They propose a protocol to construct an ImageNet-style hierarchy representative of more languages and cultures. |
| Outcome: | The proposed protocol lets the selection of concepts and images be entirely driven by native speakers, rather than scraping them automatically. |
COMETA: A Corpus for Medical Entity Linking in the Social Media (2020.emnlp-main)
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| Challenge: | Existing datasets for Entity Linking (EL) fail to address the complex nature of health terminology in layman’s language. |
| Approach: | They propose to use a corpus of 20k English biomedical entity mentions from Reddit expert-annotated with links to a widely-used medical knowledge graph to investigate the ability of these systems to perform complex inference on entities and concepts. |
| Outcome: | The proposed corpus satisfies a combination of desirable properties, from scale and coverage to diversity and quality, that to the best of our knowledge has not been met by existing resources in the field. |
S3Eval: A Synthetic, Scalable, Systematic Evaluation Suite for Large Language Model (2024.naacl-long)
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| Challenge: | Existing benchmarks fail to evaluate extremely long-context LLMs or analyze their limitations. |
| Approach: | They propose a Synthetic, Scalable, Systematic evaluation suite for LLMs using SQL execution. |
| Outcome: | The proposed evaluation suite is able to scale text length and difficulty across scenarios and provides strong correlations with real-world benchmarks. |
DePlot: One-shot visual language reasoning by plot-to-table translation (2023.findings-acl)
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Fangyu Liu, Julian Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, Yasemin Altun
| Challenge: | Existing models for visual language reasoning require tens of thousands of training examples and their reasoning capabilities are limited. |
| Approach: | They propose a one-shot solution to visual language reasoning by combining plot-to-text translation and reasoning over the translated text into a modality conversion module. |
| Outcome: | The proposed method improves on human-written queries on plots and charts compared with a fine-tuned SOTA model on human queries. |
Rewire-then-Probe: A Contrastive Recipe for Probing Biomedical Knowledge of Pre-trained Language Models (2022.acl-long)
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| Challenge: | Despite the growing progress of probing knowledge for pre-trained language models, specialised areas such as the biomedical domain are vastly under-explored. |
| Approach: | They propose a biomedical knowledge probing benchmark, MedLAMA, constructed based on the Unified Medical Language System (UMLS) Metathesaurus. |
| Outcome: | The proposed approach pushes the acc@10 to 28%, but the performance gap remains notable. |
Plot Twist: Multimodal Models Don’t Comprehend Simple Chart Details (2024.findings-emnlp)
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| Challenge: | Recent advances in multimodal models show remarkable performance in real-world benchmarks for chart and figure understanding like ChartQA that involve interpreting trends, comparing data points, and extracting insights from visuals. |
| Approach: | They propose to ask models basic questions about axes ranges and values to examine their visual understanding abilities in the context of charts. |
| Outcome: | The models perform well on complex tasks, but lack basic capabilities on basic questions. |
Prix-LM: Pretraining for Multilingual Knowledge Base Construction (2022.acl-long)
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| Challenge: | Existing methods to build and enrich multilingual knowledge bases have not been successful . knowledge expressed in different languages may be complementary and unequally distributed . |
| Approach: | They propose a model that integrates useful multilingual and KB-based factual knowledge into a single model. |
| Outcome: | The proposed model can provide richer combined knowledge than monolingual KBs. |
Fine-Grained Controllable Text Generation Using Non-Residual Prompting (2022.acl-long)
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| Challenge: | Existing approaches to control the text generation process are not expressive enough. |
| Approach: | They propose an encoder-decoder architecture that enables intermediate text prompts at arbitrary time steps. |
| Outcome: | The proposed architecture is expressive and versatile on multiple experimental settings. |
Do ever larger octopi still amplify reporting biases? Evidence from judgments of typical colour (2022.aacl-short)
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| Challenge: | Language models trained on text-only corpora have no direct access to the physical world and thus suffer from reporting bias. |
| Approach: | They investigate reporting bias from the perspective of colour in larger language models such as PaLM and GPT-3. |
| Outcome: | The proposed models outperform smaller models on the basis of colour and more closely track human judgements than smaller models. |
A Strong and Robust Baseline for Text-Image Matching (P19-2)
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| Challenge: | Text-image matching is one of the most popular methods for training text-image embeddings. |
| Approach: | They propose to use a kNN-margin loss that utilizes hard negatives and is robust to noise . they advocate using Inverted Softmax and Cross-modal Local Scaling during inference . |
| Outcome: | The proposed loss function is robust to noise and pseudo negatives are tolerable . the proposed loss functions improve scores of all metrics by a large margin . |
Improving Bilingual Lexicon Induction with Cross-Encoder Reranking (2022.findings-emnlp)
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| Challenge: | Current methods for bilingual lexicon induction rely on the induction of cross-lingual word embeddings (CLWEs) such as VecMap or mPLMs are not available for multilingual NLP. |
| Approach: | They propose a semi-supervised post-hoc reranking method which combines cross-lingual lexical knowledge from multilingual pretrained language models with original CLWEs. |
| Outcome: | The proposed method outperforms existing methods on two standard benchmarks spanning a wide spectrum of languages and is robust to different CLWEs. |
How to tackle an emerging topic? Combining strong and weak labels for Covid news NER (2022.aacl-short)
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| Challenge: | Named Entity Recognition (NER) models can be trained for emerging topics such as medical domain where new topics are constantly evolving out of the scope of existing models and datasets. |
| Approach: | They propose a recipe to combine weak and strong labels to improve Named Entity Recognition (NER) models for emerging topics. |
| Outcome: | The proposed model outperforms methods trained on weak data while combining out-of-domain and in-domain weak label training. |
Teaching Small Language Models to Reason for Knowledge-Intensive Multi-Hop Question Answering (2024.findings-acl)
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| Challenge: | Large language models can teach small language models to solve complex reasoning tasks by Chain-of-thought Distillation (CoTD) e.g., mathematical question answering. |
| Approach: | They propose a method that distills two student models to solve a multi-hop question . they use chain-of-thought distillation to generate step-by-step reasoning paths . |
| Outcome: | The proposed method surpasses existing methods on knowledge-intensive multi-hop questions. |
TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning (2022.findings-naacl)
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| Challenge: | Existing pre-trained MLMs produce an anisotropic distribution of token representations . this is not ideal for tasks that require discriminative semantic meanings of distinct tokens - a problem that exists in pre-training models . |
| Approach: | They propose a continual pre-training approach that encourages BERT to learn an isotropic distribution of token representations. |
| Outcome: | The proposed approach improves on a wide range of English and Chinese benchmarks. |
GATE: Graph-based Adaptive Tool Evolution Across Diverse Tasks (2026.acl-long)
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Jianwen Luo, Yiming Huang, Jinxiang Meng, Fangyu Lei, Shizhu He, Xiao Liu, Shanshan Jiang, Bin Dong, Jun Zhao, Kang Liu
| Challenge: | Existing toolsets that use large language models are limited to single-task settings. |
| Approach: | They propose a framework that dynamically constructs and evolves a hierarchical graph of reusable tools across multiple scenarios. |
| Outcome: | The proposed framework achieves up to 4.3 faster milestone completion in Minecraft compared to the previous state-of-the-art method and provides an average improvement of 9.23% over existing tool-making methods in code generation tasks and 10.03% in agent tasks. |
Revisiting Parameter-Efficient Tuning: Are We Really There Yet? (2022.emnlp-main)
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| Challenge: | Pretrained language models (PLMs) are used as backbones to be combined with additional parameters and finetuned on downstream tasks in an end-to-end manner. |
| Approach: | They propose to use a fraction of parameters to tune pretrained language models (PLMs) this is the first comprehensive investigation into the training and evaluation of PETuning methods. |
| Outcome: | The proposed methods have been validated and tested with a rigorous evaluation protocol and have shown that they are unstable and inconsistent. |
S3HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering (2023.acl-short)
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| Challenge: | Existing question answering systems use a retriever-reader framework to answer multi-hop questions . existing models lack retrieval, selector, and reasoner capabilities . |
| Approach: | They propose a three-stage text tableQA framework which comprises of retriever, selector, and reasoner. |
| Outcome: | The proposed framework outperforms baseline methods in the few-shot setting and ranks first on the HybridQA leaderboard. |
Contrastive Out-of-Distribution Detection for Pretrained Transformers (2021.emnlp-main)
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| Challenge: | Pretrained Transformers achieve remarkable performance when training and test data are from the same distribution, but in real-world scenarios, out-of-distribution instances can cause semantic shift problems. |
| Approach: | They propose to fine-tune the Transformers with a contrastive loss, which improves the compactness of representations, and to use the Mahalanobis distance in the model's penultimate layer to detect OOD instances. |
| Outcome: | The proposed method outperforms baselines in the real-world and achieves near-perfect OOD detection performance. |
Reranking Overgenerated Responses for End-to-End Task-Oriented Dialogue Systems (2024.lrec-main)
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| Challenge: | End-to-end task-oriented dialogue systems fall into the so-called ‘likelihood trap’, resulting in generated responses which are dull, repetitive, and inconsistent with dialogue history. |
| Approach: | They propose a reranking method to select high-quality items from the initial overgenerated list without the availability of the gold response. |
| Outcome: | The proposed method is based on a multi-woz dataset and human evaluation. |
Self-Alignment Pretraining for Biomedical Entity Representations (2021.naacl-main)
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| Challenge: | Existing approaches to self-supervised learning of biomedical entities are limited in the biomedic domain. |
| Approach: | They propose a pretraining scheme that self-aligns the representation space of biomedical entities. |
| Outcome: | The proposed framework achieves state-of-the-art on six MEL benchmarking datasets. |
Sharpness-Aware Minimization with Dynamic Reweighting (2022.findings-emnlp)
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| Challenge: | Deep neural networks are often overparameterized and can overfit training data. |
| Approach: | They propose an adversarial weight minimization algorithm that conducts adversarials and finds a common adversaria per-batch. |
| Outcome: | The proposed algorithm finds a common adversarial weight perturbation per-batch. |
TweetNLP: Cutting-Edge Natural Language Processing for Social Media (2022.emnlp-demos)
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Jose Camacho-collados, Kiamehr Rezaee, Talayeh Riahi, Asahi Ushio, Daniel Loureiro, Dimosthenis Antypas, Joanne Boisson, Luis Espinosa Anke, Fangyu Liu, Eugenio Martínez Cámara
| Challenge: | TweetNLP is an integrated platform for natural language processing in social media. |
| Approach: | They propose a Python-based platform for natural language processing in social media that supports a variety of NLP tasks. |
| Outcome: | The proposed platform supports generic focus areas such as sentiment analysis and named entity recognition, as well as social media-specific tasks such as emoji prediction and offensive language identification. |
DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models (2024.emnlp-main)
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Yiming Huang, Jianwen Luo, Yan Yu, Yitong Zhang, Fangyu Lei, Yifan Wei, Shizhu He, Lifu Huang, Xiao Liu, Jun Zhao, Kang Liu
| Challenge: | DA-Code is a code generation benchmark designed to assess LLMs on agent-based data science tasks. |
| Approach: | They propose a code generation benchmark specifically designed for LLMs on agent-based data science tasks. |
| Outcome: | The benchmark performs better than existing frameworks, but lacks accuracy . it is based on real-world data, and includes examples that cover a wide range of tasks . |
Improving Word Translation via Two-Stage Contrastive Learning (2022.acl-long)
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| Challenge: | Existing approaches to bilingual lexicon induction (BLI) are limited to two stages, but we propose a robust and effective two-stage contrastive learning framework for the task. |
| Approach: | They propose a two-stage contrastive learning framework for the task . they propose to refine cross-lingual linear maps between static word embeddings via a contrastive objective and integrate it into the self-learning procedure for even more refined cross-linguistic maps. |
| Outcome: | The proposed framework improves cross-lingual maps and word translation capability by integrating it into the self-learning procedure. |
Compositional Zero-Shot Domain Transfer with Text-to-Text Models (2023.tacl-1)
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Fangyu Liu, Qianchu Liu, Shruthi Bannur, Fernando Pérez-García, Naoto Usuyama, Sheng Zhang, Tristan Naumann, Aditya Nori, Hoifung Poon, Javier Alvarez-Valle, Ozan Oktay, Stephanie L. Hyland
| Challenge: | Existing approaches to zero-shot domain transfer are limited by domain gap and lack of in-domain labels. |
| Approach: | They propose a compositional transfer learning framework (DoT51) that learns domain knowledge and task knowledge in a multi-task manner without access to in-domain labels. |
| Outcome: | The proposed framework outperforms the current state-of-the-art in zero-shot domain transfer by over 7 absolute points in accuracy on RadNLI. |
On-the-fly Denoising for Data Augmentation in Natural Language Understanding (2024.findings-eacl)
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| Challenge: | Existing methods to improve data augmentation performance may introduce noisy data that impairs training. |
| Approach: | They propose an on-the-fly denoising technique that learns from soft augmented labels provided by an organic teacher model trained on the cleaner original dataset. |
| Outcome: | The proposed method improves on text classification and question-answering tasks on general augmentation techniques and prevents overfitting on noisy labels. |
Competition-Level Problems are Effective LLM Evaluators (2024.findings-acl)
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Yiming Huang, Zhenghao Lin, Xiao Liu, Yeyun Gong, Shuai Lu, Fangyu Lei, Yaobo Liang, Yelong Shen, Chen Lin, Nan Duan, Weizhu Chen
| Challenge: | Large language models (LLMs) have demonstrated impressive reasoning capabilities, yet there is ongoing debate about their capabilities and the potential data contamination problem. |
| Approach: | They propose to evaluate the reasoning capabilities of large language models in solving recent competition-level programming problems in Codeforces. |
| Outcome: | The proposed model has experienced a cliff-like decline in problems after September 2021, which shows the potential data contamination and the challenges for any existing LLM to solve unseen complex reasoning problems. |
POSQA: Probe the World Models of LLMs with Size Comparisons (2023.findings-emnlp)
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| Challenge: | Embodied language comprehension emphasizes that language understanding is not only mental processing in the brain but also involves interactions with the physical and social environment. |
| Approach: | They propose to use a physical object size question to examine the extremity of large language models to test their embodied comprehension. |
| Outcome: | The proposed dataset shows that even the largest LLMs perform poorly under the zero-shot setting. |
Visual Spatial Reasoning (2023.tacl-1)
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| Challenge: | Existing benchmarks for testing vision-language models (VLMs) are not ideal as they conflate multiple sources of error and do not allow controlled analysis on specific linguistic or cognitive properties. |
| Approach: | They present a dataset containing more than 10k natural text-image pairs with 66 types of spatial relations in English (e.g., under, in front of, facing). |
| Outcome: | The proposed model fails to capture relational information in a visual question answering task and referring expression comprehension tasks. |
Faithful Chart Summarization with ChaTS-Pi (2024.acl-long)
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| Challenge: | Chart-to-summary generation can help explore data, communicate insights, and help the visually impaired. |
| Approach: | They propose a reference-free chart summarization metric for scoring faithfulness . they use human raters to fix and rank candidate summaries from any chart-summarization model . |
| Outcome: | The proposed metric scores the summarization faithfulness according to human ratings better than reference-based metrics, either learned or n-gram based. |