Papers by Wray Buntine
Logical Reasoning with Outcome Reward Models for Test-Time Scaling (2025.emnlp-main)
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| Challenge: | Logical reasoning is a critical benchmark for evaluating the capabilities of large language models (LLMs), but it is under-explored in deductive reasoning. |
| Approach: | They propose to use Chain-of-Thought to generate data using single and multiple samples to train ORMs. |
| Outcome: | The proposed model expands the type of errors covered in the training dataset, covering previously unexplored error types. |
Improving Vietnamese-English Medical Machine Translation (2024.lrec-main)
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| Challenge: | Existing high-quality Vietnamese-English parallel datasets are inadequate for translation training. |
| Approach: | They introduce a high-quality Vietnamese-English parallel dataset for medical translation . they compare Google Translate, ChatGPT, and pre-trained bilingual/multilingual models . |
| Outcome: | The proposed dataset is compared with translation models from Google Translate and ChatGPT. |
Assessing the Sensitivity and Alignment of FOL Closeness Metrics (2025.findings-emnlp)
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| Challenge: | a recent paradigm of logical reasoning with tool-augmented large language models has been successful, but the correctness of FOL statements often go unverified due to the lack of a reliable evaluation metric. |
| Approach: | They compare sensitivity of existing metrics and their alignment with LLM to evaluate FOLs. |
| Outcome: | The proposed metrics improve the sensitivity and robustness of the proposed models. |
Discrete Diffusion Language Model for Efficient Text Summarization (2025.findings-naacl)
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| Challenge: | Existing discrete diffusion models fail on conditional long-text generation due to incompatibility between the backbone architectures and the random noising process. |
| Approach: | They propose a semantic-aware noising process that enables Transformer backbones to handle long sequences effectively. |
| Outcome: | The proposed model outperforms existing models on three benchmark summarization datasets while achieving much faster inference speed compared to autoregressive models. |
Topic Model or Topic Twaddle? Re-evaluating Semantic Interpretability Measures (2021.naacl-main)
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| Challenge: | Existing methods for topic model evaluation use automated measures modeled on human evaluation tests that are dissimilar to applied usage. |
| Approach: | They propose to use a novel experimental framework to evaluate topic models and assess their coherence for specialized collections in an applied setting. |
| Outcome: | The proposed framework is reflective of human evaluations using open labeling, typical of applied research. |
Neural Attention-Aware Hierarchical Topic Model (2021.emnlp-main)
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| Challenge: | Neural topic models (NTMs) use deep neural networks to learn topic information. |
| Approach: | They propose a variational autoencoder model that reconstructs sentence and document word counts using bag-of-words embeddings and pre-trained semantic embedders. |
| Outcome: | The proposed model lowers reconstruction errors at sentence and document levels and finds more coherent topics from real-world datasets. |
Multilingual Neural Machine Translation: Can Linguistic Hierarchies Help? (2021.findings-emnlp)
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| Challenge: | Multilingual Neural Machine Translation (MNMT) trains a single model that supports translation between multiple languages . transferring knowledge from a diverse set of languages degrades the translation performance due to negative transfer. |
| Approach: | They propose a hierarchical knowledge distillation approach to train multilingual models . they use typological features and phylogeny to overcome negative transfer issue . |
| Outcome: | The proposed approach avoids negative transfer effect by capitalising on language groups generated according to typological features and phylogeny of languages. |
PiVe: Prompting with Iterative Verification Improving Graph-based Generative Capability of LLMs (2024.findings-acl)
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| Challenge: | Existing large language models are pre-trained on unstructured data, which leads to poor performance when dealing with structured data. |
| Approach: | They propose a framework to train large language models to act as verifier modules and to apply iterative corrections offline. |
| Outcome: | The proposed framework improves graph-based generative capability of large language models by iterating corrective instructions on three graph-derived datasets. |
On the Effect of Isotropy on VAE Representations of Text (2022.acl-short)
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| Challenge: | Injecting desired geometric properties into text representations has attracted a lot of attention due to its better utilisation of representation space. |
| Approach: | They propose to use an isotropic Gaussian posterior instead of the ellipsoidal Gausssian priori to inject isotropy into text representations. |
| Outcome: | The proposed method improves classification performance, robustness to input perturbation, and generative behavior compared to the ellipsoidal Gaussian posterior. |
MTP: A Dataset for Multi-Modal Turning Points in Casual Conversations (2024.acl-short)
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| Challenge: | a new problem setting is designed to detect critical moments in conversations . a human-annotated multi-modal dataset is used to classify and detect turning points . |
| Approach: | They propose a problem setting focusing on turning points in conversations as TPs . they propose MTPC, MTPD, & MTPR tasks to classify and detect turning points . |
| Outcome: | The proposed model achieves an F1-score of 0.88 in classification and 0.61 in detection . it uses state-of-the-art vision-language models to construct a narrative from the videos . |
Collective Wisdom: Improving Low-resource Neural Machine Translation using Adaptive Knowledge Distillation (2020.coling-main)
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| Challenge: | Existing approaches to train high-quality NMT models in bilingually low-resource scenarios are limited by the scarcity of parallel sentence-pairs. |
| Approach: | They propose to distill the knowledge of teacher models to a single student model by using knowledge distillation. |
| Outcome: | The proposed approach achieves up to +0.9 BLEU score improvements compared to strong baselines. |
Adaptive Knowledge Sharing in Multi-Task Learning: Improving Low-Resource Neural Machine Translation (P18-2)
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| Challenge: | Neural Machine Translation (NMT) requires large amounts of bilingual data to learn a translation model with reasonable quality. |
| Approach: | They propose to extend recurrent units with multiple "blocks" along with a trainable "routing network" this allows for adaptive collaboration by dynamic sharing of blocks conditioned on the task at hand, input, and model state. |
| Outcome: | Empirical evaluations of two low-resource translation tasks show +1 BLEU score improvements compared to strong baselines. |
Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios (2022.emnlp-main)
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| Challenge: | Named Entity Recognition (NER) tasks require a large amount of training data and domains are often scarcely labeled. |
| Approach: | They propose a hardness-guided domain adaptation framework for bioNER tasks that leverages domain hardness information to improve the adaptability of the learnt model in low-resource scenarios. |
| Outcome: | The proposed model outperforms the state-of-the-art MetaNER model on biomedical datasets. |
Learning How to Actively Learn: A Deep Imitation Learning Approach (P18-1)
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| Challenge: | Experimental results show that heuristic-based active learning methods are limited when the data distribution of the underlying learning problems vary. |
| Approach: | They propose a method that learns an AL "policy" using "imitation learning" they use an efficient "algorithmic expert" which provides the policy learner with good actions in the encountered AL situations. |
| Outcome: | The proposed method is more effective than previous methods on two tasks . labeled data is rare while unlabelled data is abundant . |
Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence (2021.findings-emnlp)
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| Challenge: | Existing models for text segmentation use supervised and unsupervised learning to perform tasks such as text summarization and keyword extraction. |
| Approach: | They propose a transformer over transformer framework to perform neural text segmentation. |
| Outcome: | The proposed framework outperforms state-of-the-art models in terms of semantic coherence measure . bottom-level sentence encoders pre-trained on specific languages yield better performance . |
Reward Engineering for Generating Semi-structured Explanation (2024.findings-eacl)
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| Challenge: | Unstructured natural language explanations lack a comprehensive explanation mechanism to verify a model's true reasoning capabilities. |
| Approach: | They propose a reward engineering method which uses semi-structured explanations to verify a model's true reasoning capabilities. |
| Outcome: | The proposed method achieves new state-of-the-art on two semi-structured explanation generation benchmarks (ExplaGraph and COPA-SSE) . |
Towards Uncertainty-Aware Language Agent (2024.findings-acl)
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| Challenge: | Existing Language Agents neglect the notion of uncertainty during interactions with external worlds. |
| Approach: | They propose a framework that orchestrates the interaction between the agent and the external world using uncertainty quantification. |
| Outcome: | The proposed framework improves performance on 3 representative tasks and lowers reliance on external world. |
Leveraging Meta Information in Short Text Aggregation (P19-1)
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| Challenge: | Existing topic models infer topics based on word co-occurrence information, which results in degraded performance and degrades performance. |
| Approach: | They propose a generative model that aggregates short texts into clusters by leveraging the associated meta information. |
| Outcome: | The proposed model can generate more interpretable topics and document clusters. |
VerifiAgent: a Unified Verification Agent in Language Model Reasoning (2025.findings-emnlp)
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| Challenge: | Existing verification methods are typically model-specific or domain-restricted . Existing methods require significant computational resources and lack scalability . |
| Approach: | a unified verification agent integrates two levels of verification: meta-verification and tool-based adaptive verification. |
| Outcome: | The proposed agent outperforms baseline verification methods among reasoning tasks. |