Papers by Xueliang Zhao
There Are a Thousand Hamlets in a Thousand People’s Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory (2022.acl-long)
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| Challenge: | Existing methods for knowledge selection focus on relevance between knowledge and dialogue context, ignoring personal preference for knowledge. |
| Approach: | They propose to introduce personal memory into knowledge selection in chatbots to address personalization issue by integrating personal memory and inverse mapping into a closed loop. |
| Outcome: | The proposed method outperforms existing methods significantly on automatic evaluation and human evaluation. |
Collaborative Reasoning on Multi-Modal Semantic Graphs for Video-Grounded Dialogue Generation (2022.findings-emnlp)
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| Challenge: | Existing methods for video-grounded dialogue generation do not allow information from different modalities to complement each other. |
| Approach: | They propose a video-grounded dialogue generation model that integrates video data into pre-trained language models to allow information from different modalities to complement each other. |
| Outcome: | The proposed model outperforms state-of-the-art models on automatic and human evaluations on two public datasets. |
Logic Unveils Truth, While Disguise Obscures It: Transition Logic Augmented Response Selection for Multi-Turn Dialogue (2023.findings-emnlp)
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| Challenge: | Existing methods of negative samples tend to yield false negatives due to one-to-many property in open-domain dialogue. |
| Approach: | They propose a sequential variational ladder auto-encoder to capture one-to-many transition pattern of multiple characteristics in open-domain dialogue. |
| Outcome: | The proposed approach improves the performance of a retrieval dialogue system on two benchmarks. |
BBA: Bi-Modal Behavioral Alignment for Reasoning with Large Vision-Language Models (2024.findings-acl)
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Xueliang Zhao, Xinting Huang, Tingchen Fu, Qintong Li, Shansan Gong, Lemao Liu, Wei Bi, Lingpeng Kong
| Challenge: | Multimodal reasoning is a key capability for large vision-language models . however, the vanilla Chain-of-Thought method fails to address critical steps in multi-step reasoning tasks. |
| Approach: | They propose a bi-modal Behavioral Alignment method to augment multimodal reasoning . they use domain-specific language to integrate multimodal information into a precise alternative form . |
| Outcome: | The proposed method significantly improves GPT-4V(ision) on geometry problem solving, chess positional advantage prediction and molecular property prediction. |
Towards Efficient Dialogue Pre-training with Transferable and Interpretable Latent Structure (2022.emnlp-main)
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| Challenge: | Existing models that use millions of parameters on massive data are inefficient and lack interpretability. |
| Approach: | They propose a model with a latent structure that is easily transferable from the general domain to downstream tasks in a lightweight and transparent way. |
| Outcome: | The proposed model performs better than four strong baseline models in terms of automatic and human evaluations and is 5x faster than the strongest baseline model. |
Knowledge-Grounded Dialogue Generation with Pre-trained Language Models (2020.emnlp-main)
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| Challenge: | Empirical results indicate that pre-trained language models can significantly outperform state-of-the-art methods in both automatic evaluation and human judgment. |
| Approach: | They propose to equip a pre-trained language model with a knowledge selection module to generate knowledge-grounded dialogues. |
| Outcome: | The proposed model outperforms state-of-the-art methods in evaluation and human judgment. |
GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem Solvers (2024.acl-long)
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| Challenge: | Large language models (LLMs) have demonstrated impressive performance across various mathematical reasoning benchmarks. |
| Approach: | They introduce an adversarial grade school math dataset and explore whether LLMs can be more robust when questions are slightly changed. |
| Outcome: | The proposed method generates and verifies each intermediate thought based on its reasoning goal and calculation result. |
PromptCoT: Synthesizing Olympiad-level Problems for Mathematical Reasoning in Large Language Models (2025.findings-acl)
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| Challenge: | PromptCoT is a novel approach for synthesizing Olympiad-level math problems . it integrates rationale generation and mathematical concepts to generate complex problems based on concepts and rationale behind problem construction. |
| Approach: | They propose a method for automatically generating high-quality Olympiad-level math problems . they use mathematical concepts and the rationale behind problem construction to synthesize complex problems based on mathematical concepts . |
| Outcome: | The proposed method outperforms existing problem generation methods on benchmarks including GSM8K, MATH-500, and AIME2024. |
Learning to Express in Knowledge-Grounded Conversation (2022.naacl-main)
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| Challenge: | Existing models focus on synthesizing a dialogue with proper knowledge, but neglect that the same knowledge could be expressed differently even under the same context. |
| Approach: | They propose a model that ground dialogue generation by extra knowledge by analyzing the structure of the response and the content style of each part. |
| Outcome: | The proposed model can learn the structure style defined by a few examples and generate responses in desired content style. |
VSTAR: A Video-grounded Dialogue Dataset for Situated Semantic Understanding with Scene and Topic Transitions (2023.acl-long)
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| Challenge: | Existing benchmarks for video-grounded dialogues neglect the intrinsic attributes of multimodal dialogues, such as scene and topic transitions. |
| Approach: | They propose to use a large scale video-grounded scene&topic AwaRe dialogue dataset to study video-based dialogue understanding. |
| Outcome: | The proposed dataset shows that multimodal information and segments are important in video-grounded dialogue understanding and generation. |
There Is No Standard Answer: Knowledge-Grounded Dialogue Generation with Adversarial Activated Multi-Reference Learning (2022.emnlp-main)
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| Challenge: | Existing methods emphasize selecting one golden knowledge given a particular dialogue context, overlooking the one-to-many phenomenon in dialogue. |
| Approach: | They propose to use a multi-reference dataset to assess the one-to-many efficacy of existing KGC models. |
| Outcome: | The proposed model improves the mapping relationship between multiple knowledge and multiple responses by optimizing the model in a wake-sleep style. |
SORTIE: Dependency-Aware Symbolic Reasoning for Logical Data-to-text Generation (2023.findings-acl)
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| Challenge: | Existing studies on logical data-to-text generation rely on neural language models to generate the final table description, but they have difficulty working out key entities in the description. |
| Approach: | They propose a symbolic reasoning framework that reasons out each entity in the table description with a table-compatible programming language. |
| Outcome: | The proposed framework outperforms existing methods on three datasets and three backbones with an absolute improvement of 5.7%11.5% on SP-Acc. |
SEGO: Sequential Subgoal Optimization for Mathematical Problem-Solving (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks, including mathematical problem-solving. |
| Approach: | They propose a framework that connects the subgoal breakdown process and the probability of solving problems by identifying better subgoals with theoretical guarantees. |
| Outcome: | The proposed framework outperforms existing methods on two benchmarks, GSM8K and MATH, highlighting the potential of SEGO in AI-driven mathematical problem-solving. |
On the Compositional Generalization in Versatile Open-domain Dialogue (2023.acl-long)
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| Challenge: | Existing approaches to multi-task learning suffer from interference among datasets or fail to effectively reuse knowledge and skills learned from other datasets. |
| Approach: | They propose a sparsely activated modular network with a well-rounded set of operators and instantiate each operator with an independent module. |
| Outcome: | The proposed model outperforms state-of-the-art supervised approaches on 4 datasets with only 10% training data thanks to the modular architecture and multi-task learning. |