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Large Language Models (LLMs) have achieved impressive accomplishments in recent years. However, the increasing memory consumption of KV cache has possessed a significant challenge to the inference system. Eviction methods have revealed the inherent redundancy within the KV cache, demonstrating its potential for reduction, particularly in deeper layers. However, KV cache reduction for shallower layers has been found to be insufficient. Based on our observation that, the KV cache exhibits a high degree of similarity. Based on this observation, we proposed a novel KV cache reduction method, SpindleKV, which balances both shallow and deep layers. For deep layers, we employ an attention weight based eviction method, while for shallow layers, we apply a codebook based replacement approach which is learnt by similarity and merging policy. Moreover, SpindleKV addressed the Grouped-Query Attention (GQA) dilemma faced by other attention based eviction methods. Experiments on two common benchmarks with three different LLMs shown that SpindleKV obtained better KV cache reduction effect compared to baseline methods, while preserving similar or even better model performance.

ACL 2025

SpindleKV: A Novel KV Cache Reduction Method Balancing Both Shallow and Deep Layers

kv cache

compression

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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### 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.* 
[Read more](https://drive.google.com/file/d/1GI_hvOpjswAuYdUTromfeDiPpCcqidwg/view?usp=sharing)

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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

How can we quantize large language models while preserving accuracy? Quantization is essential for deploying large language models (LLMs) efficiently. Binary-coding quantization (BCQ) and uniform quantization (UQ) are promising quantization schemes that have strong expressiveness and optimizability, respectively. However, neither scheme leverages both advantages. In this paper, we propose UniQuan$_F$ (Unified Quantization with Flexible Mapping), an accurate quantization method for LLMs. UniQuan$_F$ harnesses both strong expressiveness and optimizability by unifying the flexible mapping technique in UQ and BCQ’s non-uniform quantization levels. We propose unified initialization, and local and periodic mapping techniques to optimize the parameters in UniQuan$_F$ precisely. After optimization, our unification theorem removes computational and memory overhead, allowing us to utilize the superior accuracy of UniQuan$_F$ without extra deployment costs induced by the unification. Experimental results demonstrate that UniQuan$_F$ outperforms existing UQ and BCQ methods, achieving up to 4.60% higher accuracy on GSM8K benchmark.

Unifying Uniform and Binary-coding Quantization for Accurate Compression of Large Language Models

With the growing demand to fit fine-grained user intents, faceted query-by-example (QBE), which retrieves similar documents conditioned on specific facets, has gained recent attention. However, prior approaches mainly depend on document-level comparisons using basic indicators like citations due to the lack of facet-level relevance datasets; yet, this limits their use to citation-based domains and fails to capture the intricacies of facet constraints. In this paper, we propose a multi-facet blending (FaBle) augmentation method, which exploits modularity by decomposing and recomposing to explicitly synthesize facet-specific training sets. We automatically decompose documents into facet units and generate (ir)relevant pairs by leveraging LLMs' intrinsic distinguishing capabilities; then, dynamically recomposing the units leads to facet-wise relevance-informed document pairs. Our modularization eliminates the need for pre-defined facet knowledge or labels. Further, to prove the FaBle's efficacy in a new domain beyond citation-based scientific paper retrieval, we release a benchmark dataset for educational exam item QBE. FaBle augmentation on 1K documents remarkably assists training in obtaining facet conditional embeddings.

Multi-Facet Blending for Faceted Query-by-Example Retrieval

Despite the widespread use of Transformer-based text embedding models in NLP tasks, surprising ``sticky tokens'' can undermine the reliability of embeddings. These tokens, when repeatedly inserted into sentences, pull sentence similarity toward a certain value, disrupting the normal distribution of embedding distances and degrading downstream performance. In this paper, we systematically investigate such anomalous tokens, formally defining them and introducing an efficient detection method, Sticky Token Detector (STD), based on sentence and token filtering. Applying STD to 40 checkpoints across 14 model families, we discover a total of 868 sticky tokens. Our analysis reveals that these tokens often originate from special or unused entries in the vocabulary, as well as fragmented subwords from multilingual corpora. Notably, their presence does not strictly correlate with model size or vocabulary size. We further evaluate how sticky tokens affect downstream tasks like clustering and retrieval, observing significant performance drops of up to 50\%. Through attention-layer analysis, we show that sticky tokens disproportionately dominate the model’s internal representations, raising concerns about tokenization robustness. Our findings show the need for better tokenization strategies and model design to mitigate the impact of sticky tokens in future text embedding applications.

Sticking to the Mean: Detecting Sticky Tokens in Text Embedding Models

Although large language models (LLMs) excel in knowledge recall and reasoning, their static nature leads to outdated information as the real world evolves or when adapting to domain-specific knowledge, highlighting the need for effective knowledge injection. However, current research on knowledge injection remains superficial, mainly focusing on knowledge memorization and retrieval. This paper proposes a four-tier knowledge injection framework that systematically defines the levels of knowledge injection: memorization, retrieval, reasoning, and association. Based on this framework, we introduce DeepKnowledge, a synthetic experimental testbed designed for fine-grained evaluation of the depth of knowledge injection across three knowledge types (novel, incremental, and updated). We then explore various knowledge injection scenarios and evaluate the depth of knowledge injection for each scenario on the benchmark. Experimental results reveal key factors to reach each level of knowledge injection for LLMs and establish a mapping between the levels of knowledge injection and the corresponding suitable injection methods, aiming to provide a comprehensive approach for efficient knowledge injection across various levels. The code is available at [https://github.com/icip-cas/Knowledge-Learning-Toolkits](https://github.com/icip-cas/Knowledge-Learning-Toolkits).

Memorizing is Not Enough: Deep Knowledge Injection Through Reasoning

In dialogue state tracking (DST), in-context learning comprises a retriever that selects labeled dialogues as in-context examples and a DST model that uses these examples to infer the dialogue state of the query dialogue. Existing methods for constructing training data for retrievers suffer from three key limitations: (1) the synergistic effect of examples is not considered, (2) the linguistic characteristics of the query are not sufficiently factored in, and (3) scoring is not directly optimized for DST performance. Consequently, the retriever can fail to retrieve examples that would substantially improve DST performance. To address these issues, we present CombiSearch—a method that scores effective in-context examples based on their combinatorial impact on DST performance. 
Our evaluation on MultiWOZ shows that retrievers trained with CombiSearch surpass state-of-the-art models, achieving a 20× gain in data efficiency and generalizing well to the SGD dataset. 
Moreover, CombiSearch attains a 12\% absolute improvement in the upper bound DST performance over traditional approaches when no retrieval errors are assumed. 
This significantly increases the headroom for practical DST performance while demonstrating that existing methods rely on suboptimal data for retriever training.

Improving Dialogue State Tracking through Combinatorial Search for In-Context Examples

Efficient processing of long contexts in large language models (LLMs) is essential for real-world applications like retrieval-augmented generation and in-context learning, especially in resource-constrained environments such as edge computing. This paper explores the embedding-based context compression to reduce inference costs while preserving the downstream LLM configurations. We propose a decoupled compressor-LLM framework, pretrained on text reconstruction and completion tasks, designed to effectively preserve essential contextual information within condensed embedding representations. Our extensive experiments investigate pretraining, model configurations, compression rates, efficiency across tasks, and adaptability to various LLMs. Results demonstrate that our approach outperforms competitive baselines in three domains and across eight datasets while being adaptable to different downstream LLMs. We find that thorough pretraining and carefully selected compression rates, such as 4x and 16x, enable a lightweight compressor to achieve a good balance between accuracy and speed. These findings underscore the potential of embedding-based compression to enhance LLM efficiency and motivate further research in this area.

Pretraining Context Compressor for Large Language Models with Embedding-Based Memory

Emotional support dialogue systems aim to reduce help-seekers' distress and help them overcome challenges. While human values—core beliefs that shape an individual's priorities—are increasingly emphasized in contemporary psychological therapy for their role in fostering internal transformation and long-term emotional well-being, their integration into emotional support systems remains underexplored. To bridge this gap, we present a value-driven method for training emotional support dialogue systems designed to reinforce positive values in seekers. Notably, our model identifies which values to reinforce at each turn and how to do so, by leveraging online support conversations from Reddit. We evaluate the method across support skills, seekers' emotional intensity, and value reinforcement. Our method consistently outperforms various baselines, effectively exploring and eliciting values from seekers. Additionally, leveraging crowd knowledge from Reddit significantly enhances its effectiveness. Therapists highlighted its ability to validate seekers' challenges and emphasize positive aspects of their situations—both crucial elements of value reinforcement. Our work, being the first to integrate value reinforcement into emotional support systems, demonstrates its promise and establishes a foundation for future research.

Dialogue Systems for Emotional Support via Value Reinforcement

Retrieval-Augmented Generation (RAG) has been proven to be an effective approach to address the hallucination problem in large language models (LLMs). In current RAG systems, LLMs typically need to synthesize knowledge provided by two main external sources (user prompts and an external database) to generate a final answer. When the knowledge provided by the user conflicts with that retrieved from the database, a critical question arises: Does the LLM favor one knowledge source over the other when generating the answer? In this paper, we are the first to unveil a new phenomenon, Authority Bias, where the LLMs tend to favor the knowledge provided by the user even when it deviates from the facts; this new phenomenon is rigorously evidenced via our novel and comprehensive characterization of Authority Bias in six widely used LLMs and across diverse task scenarios. We propose a novel dataset specifically designed for detecting Authority Bias, called the Authority Bias Detection Dataset (ABDD), and introduce new, detailed metrics to measure Authority Bias. To mitigate Authority bias, we finally propose the Conflict Detection Enhanced Query (CDEQ) framework. We identify the sentences and atomic information that generate conflicts, perform a credibility assessment on the conflicting paragraphs, and ultimately enhance the query to detect perturbed text, thereby reducing Authority bias. Comparative experiments with widely used mitigation methods demonstrate that CDEQ exhibits both effectiveness and advancement, significantly enhancing the robustness of RAG systems.

LLMs Trust Humans More, That’s a Problem! Unveiling and Mitigating the Authority Bias in Retrieval-Augmented Generation

While Large Language Models (LLMs) demonstrate remarkable capabilities, their ability to autonomously execute complex real-world tasks remains limited. Accordingly, tool learning has emerged to enable LLMs to effectively leverage external tools to extend their capabilities. Current tool-learning paradigms like CoT/ReAct employ sequential tool invocation but suffer from constrained perception and inadequate task planning. Alternative approaches using search-based decision trees incur substantial computational overhead. To address these limitations, we propose DTA-Llama (Divide-Then-Aggregate Llama), a novel parallel tool invocation framework featuring: (1) A Directed Acyclic Graph (DAG) structure that transformed from traditional tree-based tool search paths, enabling parallel execution and contributing high-quality training data; (2) A process-thread-inspired inference mechanism that iteratively decomposes tasks into parallel tool-using subtasks while aggregating results for subsequent decisions. Experimental results show that our approach substantially enhances task performance while reducing token consumption and inference time. Llama2-7B, using our method, is comparable to the official parallel function calling method of GPT-3.5. The relevant code, dataset, and model weights are available at https://corn0205.github.io/.

Divide-Then-Aggregate: An Efficient Tool Learning Method via Parallel Tool Invocation

While many advanced LLMs are designed to handle long sequence data, we can still observe notable quality degradation even within the sequence limit. In this work, we introduce a novel approach called Scaling to Emphasize Attention for Long-context retrieval (SEAL), which enhances the retrieval performance of large language models (LLMs) over long contexts. We observe that specific attention heads are closely tied to long-context retrieval, showing positive or negative correlation with retrieval scores, and adjusting the strength of these heads boosts the quality of LLMs in long context by a large margin. Built on this insight, we propose a learning-based mechanism that leverages generated data to emphasize these heads. By applying SEAL, we achieve significant improvements in long-context retrieval performance across various tasks and models. Additionally, when combined with existing training-free context extension techniques, SEAL extends the contextual limits of LLMs while maintaining highly reliable outputs.

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Unifying Uniform and Binary-coding Quantization for Accurate Compression of Large Language Models

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