Morocco

Conversational question answering increasingly relies on retrieval-augmented generation (RAG) to ground large language models (LLMs) in external knowledge. Yet, most existing studies evaluate RAG methods in isolation and primarily focus on single-turn settings. This paper addresses the lack of a systematic comparison of RAG methods for multi-turn conversational QA, where dialogue history, coreference, and shifting user intent substantially complicate retrieval. We present a comprehensive empirical study of vanilla and advanced RAG methods across eight diverse conversational QA datasets spanning multiple domains. Using a unified experimental setup, we evaluate retrieval quality and answer generation using generator and retrieval metrics, and analyze how performance evolves across conversation turns. Our results show that robust yet straightforward methods, such as reranking, hybrid BM25, and HyDE, consistently outperform vanilla RAG. In contrast, several advanced techniques fail to yield gains and can even degrade performance below the No-RAG baseline. We further demonstrate that dataset characteristics and dialogue length strongly influence retrieval effectiveness, explaining why no single RAG strategy dominates across settings. Overall, our findings indicate that effective conversational RAG depends less on method complexity than on alignment between the retrieval strategy and the dataset structure. We publish the code used.\footnote{\href{https://github.com/Klejda-A/exp-rag.git/}{GitHub Repository}}

EACL 2026

Comprehensive Comparison of RAG Methods Across Multi-Domain Conversational QA

chatrag

hyde

bm25

conversational qa

benchmarking

## Welcome to EMNLP 2024! 
We are excited to welcome you to one of the most prominent conferences in the field of Natural Language Processing. This year, EMNLP 2024 is being held in a hybrid format,
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#### *Message from the General Chair, Aline Villavicencio*
I’m delighted and honoured to welcome you to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026), taking place in the beautiful city of Rabat, in Morocco, in March 24-29, 2026. EACL is the flagship European conference of the Association and EACL 2026 proudly continues our field’s tradition of excellence in scholarship, innovation, and inclusivity. I am deeply grateful to the many volunteers whose dedication, generosity, and tireless efforts have made this conference possible.
For the first time EACL is being hosted in the African continent. This is an important milestone for our community, and we are grateful to our Moroccan hosts for enabling this historic moment by bringing this edition of EACL to Rabat. We are also delighted that the Second Arabic NLP School is co-located with EACL. We hope attendees enjoy this wonderful opportunity to strengthen ties with the Computational Linguistics communities across the African continent. *[Read full message](https://drive.google.com/file/d/14NlmHvwM6fPJuMmOvVh7K0vtQbEyv3SZ/view?usp=sharing)* 

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Welcome to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL). EACL 2026 will be held in Rabat, Morocco, from March 24–29, 2026. 

LLM-based assistants have been widely popularised after the release of ChatGPT. Concerns have been raised about their misuse in academia, given the difficulty of distinguishing between human-written and generated text. To combat this, automated techniques have been developed and shown to be effective, to some extent. However, prior work suggests that these methods often falsely flag essays from non-native speakers as generated, due to their low perplexity extracted from an LLM, which is supposedly a key feature of the detectors. We revisit these statements two years later, specifically in the Czech language setting. We show that the perplexity of texts from non-native speakers of Czech is not lower than that of native speakers. We further examine detectors from three separate families and find no systematic bias against non-native speakers. Finally, we demonstrate that contemporary detectors operate effectively without relying on perplexity.

Different Time, Different Language: Revisiting the Bias Against Non-Native Speakers in GPT Detectors

We study model routing for Large Language Model (LLM)-based systems. A model, called the router, dynamically chooses which LLM should handle a given input/query. We challenge the assumption that complex routers are necessary for generalising to new candidate LLMs. We introduce ContextualRouter, a simple meta-evaluation framework that predicts per-model performance for new queries by retrieving similar past queries and reweighting model scores with lightweight attention. During inference, the router balances estimated performance and cost by adjusting a tunable cost penalty parameter. This allows the router to adapt dynamically to the addition or removal of LLMs without the need for retraining. \n\nAcross five routing benchmarks (SPROUT, RouterBench, LiveBench, BigGenBench, and EmbedLLM), ContextualRouter matches the quality–cost trade-offs of other generalisable routers. Surprisingly, a simpler non-parametric baseline, k-nearest-neighbour averaging, performs comparably or better, achieving strong performance estimation, high NDCG, and substantial cost savings. Retrieval-based routers remain robust to k, embedding size, data sparsity, retrieval degradation, and generalise to unseen queries and models with as little as 1% historical data. These results suggest that effective retrieval alone enables generalisable LLM routing.

Generalising LLM Routing using Past Performance Retrieval: A Few-Shot Router is Sufficient

While the semantic space has been examined as a way to computationally represent language meaning-grammar interface, minimal research has been done comparing the semantic spaces of first and second language learners. We investigated the semantic space of university-level students learning French by extracting semantic features from narrative text over various time points from a 21-month period. After using machine learning models to classify native speakers' semantic features from second language learners', we used interpretability techniques to identify the most informative features per model. Through this, we discovered a variety of embedding similarity features to be decisive in language learning. We compared both groups to determine how the features differed per group and if there was any change over time. The findings demonstrated that the second language learners on average had higher semantic similarity scores than the native speakers at the token level. The similarity decreased over time but did not reach native-level values. Similarly, average surprisal was higher in the second language learner group, which steadily decreased over the course of the data collection period. These results provide insight into personalized education with more precise and effective computational indices tracking learners' progress.

Exploring the Semantic Space of Second Language Learners

While Large Language Models demonstrate expert proficiency on medical benchmarks, the clinical encounter requires more than factual retrieval. It demands a sophisticated rhetorical performance of care that balances authority with epistemic humility. This paper investigates the Clinical Fingerprint by comparing the structural and ethical integrity of advice generated by human physicians and various language models.


Our findings reveal a fundamental divergence in how clinical information is prioritized and delivered. We show that whereas physicians utilize efficient, action-oriented structures to provide clear guidance, generic models often bury critical advice under layers of complex linguistic recursion. This creates a significant cognitive load for patients and risks a dangerous safety cliff where models adopt an unearned authoritative tone. Such models frequently mimic the confidence of a doctor while providing contradictory advice, particularly in complex cases involving multiple symptoms. By identifying these rhetorical gaps, our work emphasizes that domain-specific fine-tuning is an ethical necessity to ensure that AI assistants maintain the necessary humility and logical cohesion required for safe medical practice.

The Clinical Fingerprint: Comparing the Rhetorical Integrity and Epistemic Safety of Human Physicians and Large Language Models

Fine-tuning Transformer models is often dominated by the backward computation in linear layers. In many NLP tasks, input sequences are short and padded to a fixed context length, inducing structured sparsity in the output gradients. We propose Sparsity-Exploiting Backward Pass (SEBP), a heuristic method that reduces backward computation by exploiting this sparsity with negligible memory overhead. We show that, for short input sequences, the output gradients of BERT-based and LLaMA models exhibit pronounced sparsity, allowing for optimisation in the backward computation. We optimized the autograd function in the linear layers, significantly reducing the number of FLOPs during the backward.

Our method achieves a backward pass speedup of approximately 2.15x for BERT-base on GLUE tasks and 1.99x for a 3B LLaMA model on reasoning benchmarks, while maintaining memory usage nearly identical to the regular PyTorch fine-tuning. Crucially, this speedup comes at no cost to performance. We show that our method matches standard convergence rates, offering a memory-efficient way to accelerate LLM fine-tuning.

Acceleration of Backpropagation in Linear Layers of Transformer Models Based on Gradient Structure

Understanding sarcasm requires integrating cues from language, voice, and facial expression. Recent work has achieved impressive results using large multimodal Transformers, but such models are computationally expensive and often obscure how each modality contributes to the final prediction. This paper introduces a lightweight, interpretable framework for multimodal sarcasm detection that combines frozen text, audio, and visual embeddings from pretrained encoders through compact fusion heads. Using the MUStARD++Balanced dataset, we show that early fusion of textual and acoustic features improves over the best unimodal baseline. Character-specific evaluation further shows that sarcasm expressed through overt prosodic and visual cues is substantially easier to detect than monotone, context-dependent sarcasm. Additionally, we evaluate generalization to different characters through leave-one-speaker-out (LOSO) experiments and run ablation-style transfer experiments on two speakers with similar sarcasm distributions. These findings demonstrate that effective multimodal sarcasm understanding can emerge from frozen, resource-efficient representations without large-scale fine-tuning, emphasizing the importance of modality interaction and delivery style rather than model scale.

Scale Is All You Need 🙄: Analyzing Modality Interaction and Speaker Intent Without Fine-Tuning

Automatic story generation aims to produce coherent, engaging, and contextually consistent narratives with minimal or no human involvement, thereby advancing research in computational creativity and applications in human language technologies. The emergence of large language models has progressed the task, enabling systems to generate multi-thousand-word stories under diverse constraints. Despite these advances, maintaining narrative coherence, character consistency, storyline diversity, and plot controllability in generating stories is still challenging. In this survey, we conduct a systematic review of research published over the past four years to examine the major trends and key limitations in story generation methods, model architectures, datasets, and evaluation methodologies. Based on this analysis of 57 included papers, we propose developing new evaluation metrics and creating more suitable datasets, together with ongoing improvement of narrative coherence and consistency, as well as their exploration in practical applications of story generation, as actions to support continued progress in automatic story generation.

Text-to-Text Automatic Story Generation: A Survey

Indian languages represent a highly multilingual and low-resource speech ecosystem, where the scarcity of high-quality parallel speech corpora significantly limits the development of speech-to-speech translation systems. Most existing approaches rely on cascaded pipelines that combine automatic speech recognition (ASR), machine translation (MT), and text-to-speech synthesis (TTS). While effective, these cascaded systems often suffer from cumulative error propagation, increased latency, and higher computational complexity, particularly in low-resource Indian languages. To address these challenges, my doctoral work proposes a novel sequence-to-sequence direct speech translation framework capable of translating speech from one Indian language to another without relying on intermediate text representations. Recent advances in deep learning, however, indicate that direct speech translation architectures can surpass conventional cascaded systems in both efficiency and translation quality, motivating the design of our fully end-to-end solution. We aim to release an initial dataset comprising at least 120,000 real speech samples within a 6–12 month timeframe.

Thesis Proposal: Development of End-to-End Speech Translation Models for Indian Languages

Recent advances in Sparse Autoencoders (SAEs) have revealed interpretable features within large language models (LLMs), including features that are specific to individual languages.
In prior work, these features have been used to steer a model’s output language.
However, the impact of SAE-based language steering on output quality and task performance, as well as its relationship to simpler prompting-based approaches, remains unclear.
In this work, we study the effects of language steering using SAE features across multiple tasks and models.
We apply language-specific SAE feature steering to three LLMs from two model families and evaluate it on a translation task and a multilingual question-answering task.
We compare SAE-based steering against prompting and language neuron-based steering, and examine a combined prompting-and-steering approach.
On the translation task, SAE feature steering achieves an average target-language accuracy of 92\% across models and languages, consistently outperforming language neuron-based steering, but slightly underperforming prompting in language accuracy and output quality.
In contrast, on the multilingual question-answering task, SAE-based steering enables stronger language control than prompting, and combining steering with prompting yields the best overall language control and task performance.
These findings demonstrate the potential of SAE features as a tool for controllable multilingual generation.

Evaluating the Impact of SAE-based Language Steering on LLM Performance

Police interrogation transcripts are key evidential documents, yet their linguistic form is rarely systematically analyzed, despite directly shaping judicial interpretation. This study presents the first computational forensic linguistic profiling of Italian police transcripts, focusing on the two transcription formats used in practice: narrative monologues and question-answer (Q-A) transcripts. Using automated extraction of 147 linguistic features, we analyze 50 authentic transcripts against a multi-genre Italian reference corpus to support more transparent evaluation of police transcripts by clarifying how transcription formats systematically shape evidential interpretation in judicial contexts. Narrative monologues exhibit deeper syntactic embedding, higher past-tense usage, and more first-person singular verbs, supporting coherent and temporally ordered recounting of events. Q-A transcripts, by contrast, show longer subordinate chains, more clausal complements, and higher pronoun frequency, reflecting interactive turn-taking and procedural dynamics. Rather than aiming at predictive classification, the study reveals the linguistic mechanisms shaping transcription formats and demonstrates that structurally and legally informed features reliably distinguish them. Computational models reliably capture genre-specific cues, offering scalable, empirically grounded insights into transcription practices and evidential reliability.

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Different Time, Different Language: Revisiting the Bias Against Non-Native Speakers in GPT Detectors

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Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QA

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Next from EACL 2026

Different Time, Different Language: Revisiting the Bias Against Non-Native Speakers in GPT Detectors

Similar lecture

Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QA

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

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