Austria

Multimodal coreference resolution (MCR) aims to identify mentions referring to the same entity across different modalities, such as text and visuals, and is essential for understanding multimodal content. In the era of rapidly growing multimodal content and social media, MCR is particularly crucial for interpreting user interactions and bridging text-visual references to improve communication and personalization. However, MCR research for real-world dialogues remains unexplored due to the lack of sufficient data resources. To address this gap, we introduce TikTalkCoref, the first Chinese multimodal coreference dataset for social media in real-world scenarios, derived from the popular Douyin short-video platform. This dataset pairs short videos with corresponding textual dialogues from user comments and includes manually annotated coreference clusters for both person mentions in the text and the coreferential person head regions in the corresponding video frames. We also present an effective benchmark approach for MCR, focusing on the celebrity domain, and conduct extensive experiments on our dataset, providing reliable benchmark results for this newly constructed dataset. We release the TikTalkCoref dataset to facilitate future research on MCR for real-world social media dialogues at https://github.com/lxystaruni/TikTalkCoref.

ACL 2025

Multimodal Coreference Resolution for Chinese Social Media Dialogues: Dataset and Benchmark Approach

multimodal coreference resolution

chinese social media

dataset

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



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COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

poster

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Text-based reinforcement-learning agents improve their policies by interacting with their environments to collect more training data. However, these self-collected data inevitably contain intermediate failed actions caused by attempting physically infeasible behaviors and/or hallucinations. Directly learning a policy from such trajectories can reinforce incorrect behaviors and reduce task success rates. In this paper, we propose a failed action-aware objective that suppresses the negative impact of failed actions during training by assigning zero return based on textual feedback. Building on this objective, we introduce a perturbation method that leverages unsuccessful trajectories to construct new successful ones that share the same goal. This allows agents to benefit from diverse experiences without further interaction with the environment. Experiments in ALFWorld and ScienceWorld demonstrate that our method significantly outperforms strong baselines and generalizes across environments. Code is available at https://github.com/riken-grp/text-agent.

Teaching Text Agents to Learn Sequential Decision Making from Failure

A central question in multilingual language modeling is whether large language models (LLMs) develop a universal concept representation, disentangled from specific languages. In this paper, we address this question by analyzing latent representations (latents) during a word-translation task in transformer-based LLMs. We strategically extract latents from a source translation prompt and insert them into the forward pass on a target translation prompt. By doing so, we find that the output language is encoded in the latent at an earlier layer than the concept to be translated. Building on this insight, we conduct two key experiments. First, we demonstrate that we can change the concept without changing the language and vice versa through activation patching alone. Second, we show that patching with the mean representation of a concept across different languages does not affect the models' ability to translate it, but instead improves it. Finally, we generalize to multi-token generation and demonstrate that the model can generate natural language description of those mean representations. Our results provide evidence for the existence of language-agnostic concept representations within the investigated models.

Separating Tongue from Thought: Activation Patching Reveals Language-Agnostic Concept Representations in Transformers

Enhancing the reasoning capabilities of language models (LMs) remains a key challenge, especially for tasks that require complex, multi-step decision-making where existing Chain-of-Thought (CoT) approaches struggle with consistency and verification. In this paper, we propose a novel reasoning framework, referred to as Structure-aware Planning with an Accurate World Model (SWAP), that integrates structured knowledge representation with learned planning. Unlike prior methods that rely purely on natural language reasoning, SWAP leverages entailment graphs to encode structured dependencies and enable symbolic verification of intermediate steps. To systematically construct and update the graph, SWAP employs a policy model to propose candidate expansions and a world model to predict structural updates. To improve accuracy, the world model generates multiple alternative updates, and a discriminator re-ranks them based on plausibility. To encourage diverse exploration, we introduce Diversity-based Modelling (DM), which samples candidates from the remaining probability mass after removing previously sampled candidates from the original policy distribution. Additionally, SWAP improves the discrimination accuracy through Contrastive Ranking (CR), which directly compares candidates within prompts and incorporates meta-knowledge to improve ranking quality. We evaluate SWAP across diverse reasoning-intensive benchmarks including math reasoning, logical reasoning, and coding tasks. Extensive experiments demonstrate that SWAP significantly improves upon the base models and consistently outperforms existing reasoning methods.

Deliberate Reasoning for Language Models as Structure-aware Planning with Accurate World Model

In this work, we introduce the PKU-SafeRLHF dataset, designed to promote research on safety alignment in large language models (LLMs). As a sibling project to SafeRLHF and BeaverTails, we separate annotations of helpfulness and harmlessness for question-answering pairs, providing distinct perspectives on these coupled attributes. Overall, we provide 44.6k refined prompts and 265k question-answer pairs with safety meta-labels for 19 harm categories and three severity levels ranging from minor to severe, with answers generated by Llama-family models. Based on this, we collected 166.8k preference data, including dual-preference (helpfulness and harmlessness decoupled) and single-preference data (trade-off the helpfulness and harmlessness from scratch), respectively. Using the large-scale annotation data, we further train severity-sensitive moderation for the risk control of LLMs and safety-centric RLHF algorithms for the safety alignment of LLMs. We believe this dataset will be a valuable resource for the community, aiding in the safe deployment of LLMs.

BeaverTails v2: Towards Multi-Level Safety Alignment for LLMs with Human Preference

The choice of tokenizer can profoundly impact language model performance, yet accessible and reliable evaluations of tokenizer quality remain an open challenge. Inspired by scaling consistency, we show that smaller models can accurately predict significant differences in tokenizer impact on larger models at a fraction of the compute cost. By systematically evaluating both English-centric and multilingual tokenizers, we find that tokenizer choice has negligible effects on tasks in English but results in consistent performance differences in multilingual settings. We propose new intrinsic tokenizer metrics inspired by Zipf's law that correlate more strongly with downstream performance than text compression when modeling unseen languages. By combining several metrics to capture multiple aspects of tokenizer behavior, we develop a reliable framework for intrinsic tokenizer evaluations. Our work offers a more efficient path to informed tokenizer selection in future language model development.

Beyond Text Compression: Evaluating Tokenizers Across Scales

Continued pretraining (CPT) is a popular approach to adapt existing large language models (LLMs) to new languages. When doing so, it is common practice to include a portion of English data in the mixture, but its role has not been carefully studied to date. In this work, we show that including English does not impact validation perplexity, yet it is critical for the emergence of downstream capabilities in the target language. We introduce a language-agnostic benchmark for in-context learning (ICL), which reveals catastrophic forgetting early on CPT when English is not included. This in turn damages the ability of the model to generalize to downstream prompts as measured by perplexity, even if it does not manifest in terms of accuracy until later in training, and can be tied to a big shift in the model parameters. Based on these insights, we introduce curriculum learning and exponential moving average (EMA) of weights as effective alternatives to mitigate the need for English. All in all, our work sheds light into the dynamics by which emergent abilities arise when doing CPT for language adaptation, and can serve as a foundation to design more effective methods in the future.

Emergent Abilities of Large Language Models under Continued Pre-training for Language Adaptation

Verifiers are crucial components for enhancing modern LLMs' reasoning capability. Typical verifiers require resource-intensive supervised dataset construction, which is costly and faces limitations in data diversity. In this paper, we propose LOVER, an unsupervised verifier regularized by logical rules. LOVER treats the verifier as a binary latent variable, utilizing internal activations and enforcing three logical constraints on multiple reasoning paths: negation consistency, intra-group consistency, and inter-group consistency (grouped by the final answer). By incorporating logical rules as priors, LOVER can leverage unlabeled examples and is directly compatible with any off-the-shelf LLMs. Experiments on 10 datasets demonstrate that LOVER significantly outperforms unsupervised baselines, achieving performance comparable to the supervised verifier (reaching its 95% level on average).

Logic-Regularized Verifier Elicits Reasoning from LLMs

Africa’s rich linguistic heritage remains underrepresented in NLP, largely due to historical policies that favor foreign languages and create significant data inequities. In this paper, we integrate theoretical insights on Africa’s language landscape with an empirical evaluation using Sahara— a comprehensive benchmark curated from large-scale, publicly accessible datasets capturing the continent’s linguistic diversity. By systematically assessing the performance of leading large language models (LLMs) on Sahara, we demonstrate how policy-induced data variations directly impact model effectiveness across African languages. Our findings reveal that while a few languages perform reasonably well, many Indigenous languages remain marginalized due to sparse data. Leveraging these insights, we offer actionable recommendations for policy reforms and inclusive data practices. Overall, our work underscores the urgent need for a dual approach—combining theoretical understanding with empirical evaluation—to foster linguistic diversity in AI for African communities.

Where Are We? Evaluating LLM Performance on African Languages

Citation quality is crucial in information-seeking systems, directly influencing trust and the effectiveness of information access. Current evaluation frameworks, both human and automatic, mainly rely on Natural Language Inference (NLI) to assess binary or ternary supportiveness from cited sources, which we argue is a suboptimal proxy for citation evaluation. In this work we introduce CiteEval, a citation evaluation framework driven by principles focusing on fine-grained citation assessment within a broad context, encompassing not only the cited sources but the full retrieval context, user query, and generated text. Guided by the proposed framework, we construct CiteBench, a multi-domain benchmark with high-quality human annotations on citation quality. To enable efficient evaluation, we further develop CiteEval-Auto, a suite of model-based metrics that exhibit strong correlation with human judgments. Experiments across diverse systems demonstrate CiteEval-Auto's superior ability to capture the multifaceted nature of citations compared to existing metrics, offering a principled and scalable approach to evaluate and improve model-generated citations.

CiteEval: Principle-Driven Citation Evaluation for Source Attribution

Large Language Model (LLM)-based agents exhibit significant potential across various domains, operating as interactive systems that process environmental observations to generate executable actions for target tasks. The effectiveness of these agents is significantly influenced by their memory mechanism, which records historical experiences as sequences of action-observation pairs. We categorize memory into two types: cross-trial memory, accumulated across multiple attempts, and in-trial memory (working memory), accumulated within a single attempt. While considerable research has optimized performance through cross-trial memory, the enhancement of agent performance through improved working memory utilization remains underexplored. Instead, existing approaches often involve directly inputting entire historical action-observation pairs into LLMs, leading to redundancy in long-horizon tasks. Inspired by human problem-solving strategies, this paper introduces HiAgent, a framework that leverages subgoals as memory chunks to manage the working memory of LLM-based agents hierarchically. Specifically, HiAgent prompts LLMs to formulate subgoals before generating executable actions and enables LLMs to decide proactively to replace previous subgoals with summarized observations, retaining only the action-observation pairs relevant to the current subgoal. Experimental results across five long-horizon tasks demonstrate that HiAgent achieves a twofold increase in success rate and reduces the average number of steps required by 3.8. Additionally, our analysis shows that HiAgent consistently improves performance across various steps, highlighting its robustness and generalizability. Code is available in this URL: https://github.com/HiAgent2024/HiAgent

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