Austria

Existing automatic audio generation methods struggle to generate podcast-like audio programs effectively. The key challenges lie in in-depth content generation, appropriate and expressive voice production. This paper proposed PodAgent, a comprehensive framework for creating audio programs. PodAgent 1) generates informative topic-discussion content by designing a Host-Guest-Writer multi-agent collaboration system, 2) builds a voice pool for suitable voice-role matching and 3) utilizes LLM-enhanced speech synthesis method to generate expressive conversational speech. Given the absence of standardized evaluation criteria for podcast-like audio generation, we developed comprehensive assessment guidelines to effectively evaluate the model&#39;s performance. Experimental results demonstrate PodAgent&#39;s effectiveness, significantly surpassing direct GPT-4 generation in topic-discussion dialogue content, achieving an 87.4\% voice-matching accuracy, and producing more expressive speech through LLM-guided synthesis. Demo page: https://podcast-agent.github.io/demo/. Source code: https://github.com/yujxx/PodAgent.

ACL 2025

PodAgent: A Comprehensive Framework for Podcast Generation

podcast generation

multimodal generation

multi-agent system

Colleagues and Friends:

I am very happy to welcome you to the 14th annual Canadian Neuroscience Meeting, and to the first virtual edition of our meeting. I want to highlight the important work the CAN meeting chair, **Roger Thompson** and co-chair, **Alyson Fournier**, did to put together the great program we are offering this year. Moving to a virtual format required a complete re-design of the program, and both Roger and Alyson did a splendid job in providing us with an appealing and well adapted program for our first virtual event. I wish to thank them for this very important effort.

Highlights of this year’s scientific meeting include invited speakers **Michael Hausser, Franck Polleux, Gwyneth Card, Magdalena Götz**and Brain Prize winner **Huda Zoghbi**. Plenary and parallel symposia, proposed by our members complete our diverse [scientific program.](https://can-acn.org/meeting-2021/program-2021/)

The 2021 Advocacy session, chaired and organized by CAN advocacy committee chair **Karun Singh**, will feature a special lecture and question period with Member of Parliament and past Science Minister **Kirsty Duncan**. This will be followed by a short overview of CAN’s advocacy activities over the last year. We invite you to participate in this session for insight from Dr. Duncan on the workings of Parliament Hill, and to learn more about what CAN has been doing to promote neuroscience research in Canada.

We are also excited to host **Lisa Willis** in the Equity Diversity and Inclusion session, organized by EDI committee chair **Jibran Khokhar**.

A special highlight of our meeting will be the lectures by award-winning young neuroscientists. We are please to host the CAN 2020 Young Investigator Award winner **Mihaela Iordanova**, and CAN 2021 Young Investigator Award winners **Aaron Phillips** and **Tabrez Siddiqui**. Highlights will also include presentation by the top three winners of the CAN-CIHR-INMHA Brain Star Award winners, **Claire Gizowski, Nuria Daviu Abant** and **Lukas Tamayo-Orrego**.

Independently organized satellite symposia complement and complete this exciting program, check them out here: https://can-acn.org/meeting-2021/2021-satellite-symposia/

Some of the most engaging sessions are always the poster sessions featuring the work of our trainees, so make sure to log in to Gather Town during the poster sessions to chat with the presenters.
We hope you enjoy this program, and look forward to welcoming you, virtually.

Shernaz Bamji

President of the Canadian Association for Neuroscience

![](https://assets.underline.io/uploads/markdown_image/1/image/b9aea25343a87d5aac3b9367dc780974.jpg)



Please head to https://can-acn.org/meeting-2021/registration-2021/ to register. 

CAN-ACN 2021

14th Canadian Neuroscience Meeting from August 23rd-25th

Existing automatic audio generation methods struggle to generate podcast-like audio programs effectively. The key challenges lie in in-depth content generation, appropriate and expressive voice production. This paper proposed PodAgent, a comprehensive framework for creating audio programs. PodAgent 1) generates informative topic-discussion content by designing a Host-Guest-Writer multi-agent collaboration system, 2) builds a voice pool for suitable voice-role matching and 3) utilizes LLM-enhanced speech synthesis method to generate expressive conversational speech. Given the absence of standardized evaluation criteria for podcast-like audio generation, we developed comprehensive assessment guidelines to effectively evaluate the model's performance. Experimental results demonstrate PodAgent's effectiveness, significantly surpassing direct GPT-4 generation in topic-discussion dialogue content, achieving an 87.4\% voice-matching accuracy, and producing more expressive speech through LLM-guided synthesis. Demo page: https://podcast-agent.github.io/demo/. Source code: https://github.com/yujxx/PodAgent.

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.* 
[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)

Research on reference and naming suggests that humans can come up with very different ways of conceptualizing and referring to the same object, e.g. the same abstract tangram shape can be a "crab", "sink" or "space ship". Another common assumption in cognitive science is that scene context fundamentally shapes our visual perception of objects and conceptual expectations. This paper contributes SceneGram, a dataset of human references to tangram shapes placed in different scene contexts, allowing for systematic analyses of the effect of scene context on conceptualization. Based on this data, we analyze references to tangram shapes generated by multimodal LLMs, showing that these models do not account for the richness and variability of conceptualizations found in human references.

SceneGram: Conceptualizing and Describing Tangrams in Scene Context

Large language models (LLMs) have achieved remarkable performance on knowledge graph question answering (KGQA) tasks by planning and interacting with knowledge graphs. However, existing methods often confuse tool utilization with knowledge reasoning, harming readability of model outputs and giving rise to hallucinatory tool invocations, which hinder the advancement of KGQA. To address this issue, we propose Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning (MemQ) to decouple LLM from tool invocation tasks using LLM-built query memory. By establishing a memory module with explicit descriptions of query statements, the proposed MemQ facilitates the KGQA process with natural language reasoning and memory-augmented query reconstruction. Meanwhile, we design an effective and readable reasoning to enhance the LLM's reasoning capability in KGQA. Experimental results that MemQ achieves state-of-the-art performance on widely used benchmarks WebQSP and CWQ.

Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning

We introduce the concept of context-driven over-refusal, an abstention arising when model's safety guardrails are triggered by the grounding knowledge provided alongside the user's request. Distinct from question-driven over-refusal, this occurs in both retrieval-augmented generation (RAG) and natural language processing (NLP) task completion (e.g. summarization, translation) where external content can unexpectedly trigger refusals. In this work, we present a novel two-stage evaluation framework named COVER, designed to quantify and analyze this behavior. Through a comprehensive empirical study on two public corpora, we show that over-refusal rates strongly depend on the task, system prompts, model family, and the number of retrieved documents. We observe that tasks such as translation and summarization yield disproportionately high over-refusal rates, while question-answering remains relatively robust, especially in newer models. Moreover, increasing the number of contextual documents tends to reduce refusals, yet broadens the pool of prompts at risk of encountering at least one "unsafe" text. Interestingly, strict system prompts do not necessarily lead to higher over-refusal rates, suggesting that in the absence of explicit directives, some models may default to a more cautious behavior. These findings highlight the need for fine-grained alignment and benchmarking strategies sensitive to both user intent and contextual nuances, offering a roadmap for future research in model training and evaluation.

COVER: Context-Driven Over-Refusal Verification in LLMs

The dissemination of Large Language Models (LLMs), trained at scale, and endowed with powerful text-generating abilities, has made it easier for all to produce harmful, toxic, faked or forged content. In response, various proposals have been made to automatically discriminate artificially generated from human-written texts, typically framing the problem as a binary classification problem. Early approaches evaluate an input document with a well-chosen detector LLM, assuming that low-perplexity scores reliably signal machine-made content. More recent systems instead consider two LLMs and compare their probability distributions over the document to further discriminate when perplexity alone cannot. However, using a fixed pair of models can induce brittleness in performance. We extend these approaches to the ensembling of several LLMs and derive a new, theoretically grounded approach to combine their respective strengths. Our experiments, using a variety of generator LLMs, suggest that this approach effectively harnesses each model’s capabilities, leading to strong detection performance on a variety of domains.

MOSAIC: Multiple Observers Spotting AI Content

Despite the widespread use of English as a Second or Foreign Language (ESFL), developing syntactico-semantic representations for it is limited --- the irregularities in ESFL complicate systematic composition and subsequently the derivation of its semantics. This paper draws on constructivism and proposes a novel Synchronous Hyperedge Replacement Grammar (SHRG)-based constructivist approach to address the challenges. By using constructions as fundamental units, this approach not only accommodates both the idiosyncrasies and the compositional nature of ESFL, but also bridges the gap between literal cues and intended meaning. The feasibility of this constructivist approach is demonstrated using real ESFL data, resulting in a gold-standard, medium-sized syntactico-semantic bank that covers a wide range of ESFL phenomena. Experimental results further indicate that this newly developed resource significantly enhances syntactic parsing for ESFL, achieving an F_1 score improvement of over 20%.

Compositional Syntactico-SemBanking for English as a Second or Foreign Language

Tool learning aims to augment large language models (LLMs) with diverse tools, enabling them to act as agents for solving practical tasks. Due to the limited context length of tool-using LLMs, adopting information retrieval (IR) models to select useful tools from large toolsets is a critical initial step. However, the performance of IR models in tool retrieval tasks remains underexplored and unclear. Most tool-use benchmarks simplify this step by manually pre-annotating a small set of relevant tools for each task, which is far from the real-world scenarios. In this paper, we propose ToolRet, a heterogeneous tool retrieval benchmark comprising 7.6k diverse retrieval tasks, and a corpus of 43k tools, collected from existing datasets. We benchmark six types of models on ToolRet. Surprisingly, even the models with strong performance in conventional IR benchmarks, exhibit poor performance on ToolRet. This low retrieval quality degrades the task pass rate of tool-use LLMs. As a further step, we contribute a large-scale training dataset with over 200k instances, which substantially optimizes the tool retrieval ability of IR models.

Retrieval Models Aren’t Tool-Savvy: Benchmarking Tool Retrieval for Large Language Models

The rapid growth of social media platforms has raised significant concerns regarding online content toxicity. When Large Language Models (LLMs) are used for toxicity detection, two key challenges emerge: 1) the absence of domain-specific toxicity knowledge leads to false negatives; 2) the excessive sensitivity of LLMs to toxic speech results in false positives, limiting freedom of speech. To address these issues, we propose a novel method called *MetaTox*, leveraging graph search on a meta-toxic knowledge graph to enhance hatred and toxicity detection. First, we construct a comprehensive meta-toxic knowledge graph by utilizing LLMs to extract toxic information through a three step pipeline. Second, we query the graph via retrieval and ranking processes to supplement accurate, relevant toxicity knowledge. Extensive experiments and case studies across multiple datasets demonstrate that our MetaTox boosts overall toxicity detection performance, particularly in out-of-domain settings. In addition, under in-domain scenarios, we surprisingly find that small language models are more competent. Our code is available at https://github.com/YiboZhao624/MetaTox.

Enhancing LLM-based Hatred and Toxicity Detection with Meta-Toxic Knowledge Graph

As large language models (LLMs) are applied across diverse domains, the ability to selectively unlearn specific information has become increasingly essential. For instance, LLMs are expected to provide certain confidential information to authorized internal users, such as employees or trusted partners, while withholding it from external users, including the general public and unauthorized entities. In response to this challenge, we propose a novel method termed ''in-context knowledge unlearning'', which enables the model to selectively forget information in test-time based on the query context. Our method fine-tunes pre-trained LLMs to enable prompt unlearning of target knowledge within the context, while preserving other knowledge. Experiments on TOFU, AGE and RWKU datasets using Llama2-7B/13B and Mistral-7B models show that our method achieves up to 95% forget accuracy while retaining 80% of unrelated knowledge, significantly outperforming baselines in both in-domain and out-of-domain scenarios. Further investigation of the model's internal behavior revealed that while fine-tuned LLMs generate correct predictions in the middle layers and maintain them up to the final layer, they make the decision to forget at the last layer, i.e. ''LLMs pretend to forget''. Our findings offer valuable insight into the improvement of the robustness of the unlearning mechanisms in LLM, setting a foundation for future research in the field.

Answer When Needed, Forget When Not: Language Models Pretend to Forget via In-Context Knowledge Unlearning

In this paper, we present TituLLMs, the first large pretrained Bangla LLMs, available in 1b and 3b parameter sizes. Due to computational constraints during both training and inference, we focused on smaller models. To train TituLLMs, we collected a pretraining dataset of approximately ∼ 37 billion tokens. We extended the Llama-3.2 tokenizer to incorporate language- and culture-specific knowledge, which also enables faster training and inference. There was a lack of benchmarking datasets to benchmark LLMs for Bangla. To address this gap, we developed five benchmarking datasets. We benchmarked various LLMs, including TituLLMs, and demonstrated that TituLLMs outperforms its initial multilingual versions. However, this is not always the case, highlighting the complexities of language adaptation. Our work lays the groundwork for adapting existing multilingual open models to other low-resource languages. To facilitate broader adoption and further research, we have made the TituLLMs models and benchmarking datasets publicly available.

TituLLMs: A Family of Bangla LLMs with Comprehensive Benchmarking

Documents are fundamental to preserving and disseminating information, often incorporating complex layouts, tables, and charts that pose significant challenges for automatic document understanding (DU). While vision-language large models (VLLMs) have demonstrated improvements across various tasks, their effectiveness in processing long-context vision inputs remains unclear. This paper introduces WikiMixQA, a benchmark comprising 1,000 multiple-choice questions (MCQs) designed to evaluate cross-modal reasoning over tables and charts extracted from 4,000 Wikipedia pages spanning seven distinct topics. Unlike existing benchmarks, WikiMixQA emphasizes complex reasoning by requiring models to synthesize information from multiple modalities. We evaluate 12 state-of-the-art vision-language models, revealing that while proprietary models achieve ~70% accuracy when provided with direct context, their performance deteriorates significantly when retrieval from long documents is required. Among these, GPT-4-o is the only model exceeding 50% accuracy in this setting, whereas open-source models perform considerably worse, with a maximum accuracy of 27%. These findings underscore the challenges of long-context, multi-modal reasoning and establish WikiMixQA as a crucial benchmark for advancing document understanding research.

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SceneGram: Conceptualizing and Describing Tangrams in Scene Context

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