GenAI4ELab/papercli-papers huggingface dataset:
Linguistic Units as Tokens: Intrinsic and Extrinsic Evaluation with BabyLM
Authors: Achille Fusco; Maria Letizia Piccini Bianchessi; Tommaso Sgrizzi; Asya Zanollo; Cristiano Chesi
Venue: EMNLP 2025 (Proceedings of the First BabyLM Workshop)

Abstract: Tokenization is often treated as a preprocessing step, yet in data-limited settings it directly shapes what a model can learn. We compare four segmentation strategies in the BabyLM Challenge: frequency-based BPE, morphology-aware MorPiece and ParadigmFinder, and syllable-based SylliTok. Evaluation combines two perspectives. First, an intrinsic test on the SIGMORPHON 2022 segmentation benchmark, adapted to English, measures how closely each tokenizer aligns with morpheme boundaries. Second, extrinsic tests train GPT-2 on the 10M BabyLM corpus and evaluate on the 2025 benchmark. No single tokenizer dominates. BPE remains strong on syntax-heavy tasks. ParadigmFinder excels in semantic composition and age-of-acquisition alignment. MorPiece shows advantages in discourse tracking. Morphology-aware tokenizers achieve the best intrinsic segmentation scores, and these gains translate into more robust generalisation in comprehension tasks. These results highlight tokenization as a core modeling decision, with direct consequences for compression, morphology, and the path to humanlike learning.
Batch-wise Convergent Pre-training: Step-by-Step Learning Inspired by Child Language Development
Authors: Ko Yoshida; Daiki Shiono; Kai Sato; Toko Miura; Momoka Furuhashi; Jun Suzuki
Venue: EMNLP 2025 (Proceedings of the First BabyLM Workshop)

Abstract: Human children acquire language from a substantially smaller amount of linguistic input than that typically required for training large language models (LLMs). This gap motivates the search for more efficient pre-training methods. Inspired by child development, curriculum learning, which progresses from simple to complex data, has been widely adopted. In this study, we propose a pre-training framework that mirrors child language acquisition, advancing step by step from words to sentences while retaining prior knowledge. We investigate whether this improves retention and efficiency under limited resources. Our approach is implemented through four components: (i) a curriculum-aligned dataset, (ii) a batch-wise convergence loop, (iii) a distance-controlled loss to mitigate forgetting, and (iv) a constraint-controlled optimizer for stability. Experiments on the BabyLM benchmark show that the proposed method performs slightly below the official baselines in overall accuracy, with larger gaps on grammar-oriented evaluations such as BLiMP. Nonetheless, it yields small but consistent gains on morphology- and discourse-related tasks (e.g., WUG-ADJ, Entity Tracking), suggesting that the approach affects different linguistic aspects unevenly under limited data conditions.
Pretraining Language Models with LoRA and Artificial Languages
Authors: Nalin Kumar; Mateusz Lango; Ondrej Dusek
Venue: EMNLP 2025 (Proceedings of the First BabyLM Workshop)

Abstract: Large language models (LLMs) require a substantial amount of training data, which contrasts with the data-efficient learning observed in humans. In our submission to the BabyLM Challenge, we address this disparity by proposing a parameter-efficient pretraining approach for language acquisition from limited data. Our approach involves initializing the model with token embeddings trained by a shallow model, followed by tuning the non-embedding parameters with non-linguistic data to introduce structural biases. Then, we freeze the resulting model and pretrain it on the 10M-token BabyLM corpus using LoRA adapters. Experiments on small corpora demonstrate that our approach improves upon classic pretraining of the entire model.
Masked Diffusion Language Models with Frequency-Informed Training
Authors: Despoina Kosmopoulou; Efthymios Georgiou; Vaggelis Dorovatas; Georgios Paraskevopoulos; Alexandros Potamianos
Venue: EMNLP 2025 (Proceedings of the First BabyLM Workshop)

Abstract: We present a masked diffusion language modeling framework for data-efficient training for the BabyLM 2025 Challenge. Our approach applies diffusion training objectives to language modeling under strict data constraints, incorporating frequency-informed masking that prioritizes learning from rare tokens while maintaining theoretical validity. We explore multiple noise scheduling strategies, including two-mode approaches, and investigate different noise weighting schemes within the NELBO objective. We evaluate our method on the BabyLM benchmark suite, measuring linguistic competence, world knowledge, and human-likeness. Results show performance competitive to hybrid autoregressive-masked baselines, demonstrating that diffusion-based training offers a viable alternative for data-restricted language learning.
MoEP: Modular Expert Paths for Sample-Efficient Language Modeling
Authors: Joonas Tapaninaho
Venue: EMNLP 2025 (Proceedings of the First BabyLM Workshop)

Abstract: Training language models under tight compute budgets with small training datasets remains challenging for dense decoder-only Transformers, where every token activates the full stack of model parameters. We introduce MoEP (Modular Expert Paths), a sparse decoder-only architecture that enables more selective token activation, which increases model performance and accelerates learning without increasing the total number of parameters. We show that combining model parallelism with Mixture-of-Experts (MoE) style linear projections and a lightweight top-k router outperforms the GPT-2 baseline and stabilizes evaluation performance more quickly.
RecombiText: Compositional Data Augmentation for Enhancing LLM Pre-Training Datasets in Low-Resource Scenarios
Authors: Alexander Tampier; Lukas Thoma; Loris Schoenegger; Benjamin Roth
Venue: EMNLP 2025 (Proceedings of the First BabyLM Workshop)

Abstract: We introduce RecombiText Augmentation (RTA), a novel purely statistical NLP method for compositional data augmentation for data-efficient LLM pre-training in low-resource scenarios. RTA identifies lexically and semantically similar sentences within the corpus and generates synthetic sentence pairs from them while preserving underlying patterns from the corpus. We pre-train GPT-2 and RoBERTa language models on a domain-specific, low-resource corpus of 10 million words, with different proportions of augmented data. We compare our RTA-augmented model variants to a baseline model trained on the full original dataset. Zero-shot results show that the language models pre-trained on synthetic data improve in entity tracking, self-paced reading, and morphological generalization benchmarks. In other tasks, the performance is comparable to the baseline model. We demonstrate that it is possible to expand low-resource datasets by two- to four-fold without compromising benchmark performance, solely through statistical processing of the available data.
Babies Learn to Look Ahead: Multi-Token Prediction in Small LMs
Authors: Ansar Aynetdinov; Alan Akbik
Venue: EMNLP 2025 (Proceedings of the First BabyLM Workshop)

Abstract: Multi-token prediction (MTP) is an alternative training objective for language models that has recently been proposed as a potential improvement over traditional next-token prediction (NTP). Instead of training models to predict only the next token, as is standard, MTP trains them to predict the next k tokens at each step. While MTP was shown to improve downstream performance and sample efficiency in large language models (LLMs), smaller language models (SLMs) struggle with this objective. Recently, a curriculum-based approach was offered as a solution to this problem for models as small as 1.3B parameters by adjusting the difficulty of the training objective over time. In this work we investigate the viability of MTP curricula in a highly data- and parameter-constrained setting. Our experimental results show that even 130M-parameter models benefit from including the MTP task in the pre-training objective. These gains hold even under severe data constraints, as demonstrated on both zero-shot benchmarks and downstream tasks.
CE-Bench: Towards a Reliable Contrastive Evaluation Benchmark of Interpretability of Sparse Autoencoders
Authors: Alex Gulko; Yusen Peng; Sachin Kumar
Venue: EMNLP 2025 (Proceedings of the 8th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP)

Abstract: Sparse autoencoders (SAEs) are a promising approach for uncovering interpretable features in large language models (LLMs). While several automated evaluation methods exist for SAEs, most rely on external LLMs. In this work, we introduce CE-Bench, a novel and lightweight contrastive evaluation benchmark for sparse autoencoders, built on a curated dataset of contrastive story pairs. We conduct comprehensive evaluation studies to validate the effectiveness of our approach. Our results show that CE-Bench reliably measures the interpretability of sparse autoencoders and aligns well with existing benchmarks without requiring an external LLM judge, achieving over 70% Spearman correlation with results in SAEBench. The official implementation and evaluation dataset are open-sourced and publicly available.
Char-mander Use mBackdoor! A Study of Cross-lingual Backdoor Attacks in Multilingual LLMs
Authors: Himanshu Beniwal; Sailesh Panda; Birudugadda Srivibhav; Mayank Singh
Venue: EMNLP 2025 (Proceedings of the 8th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP)

Abstract: We explore Cross-lingual Backdoor ATtacks (X-BAT) in multilingual Large Language Models (mLLMs), revealing how backdoors inserted in one language can automatically transfer to others through shared embedding spaces. Using toxicity classification as a case study, we demonstrate that attackers can compromise multilingual systems by poisoning data in a single language, with rare and high-occurring tokens serving as specific, effective triggers. Our findings reveal a critical vulnerability that affects the model’s architecture, leading to a concealed backdoor effect during the information flow. Our code and data are publicly available at https://github.com/himanshubeniwal/X-BAT.
Evil twins are not that evil: Qualitative insights into machine-generated prompts
Authors: Nathanaël Carraz Rakotonirina; Corentin Kervadec; Francesca Franzon; Marco Baroni
Venue: EMNLP 2025 (Proceedings of the 8th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP)

Abstract: It has been widely observed that language models (LMs) respond in predictable ways to algorithmically generated prompts that are seemingly unintelligible. This is both a sign that we lack a full understanding of how LMs work, and a practical challenge, because opaqueness can be exploited for harmful uses of LMs, such as jailbreaking. We present the first thorough analysis of opaque machine-generated prompts, or autoprompts, pertaining to 6 LMs of different sizes and families. We find that machine-generated prompts are characterized by a last token that is often intelligible and strongly affects the generation. A small but consistent proportion of the previous tokens are prunable, probably appearing in the prompt as a by-product of the fact that the optimization process fixes the number of tokens. The remaining tokens fall into two categories: filler tokens, which can be replaced with semantically unrelated substitutes, and keywords, that tend to have at least a loose semantic relation with the generation, although they do not engage in well-formed syntactic relations with it. Additionally, human experts can reliably identify the most influential tokens in an autoprompt a posteriori, suggesting these prompts are not entirely opaque. Finally, some of the ablations we applied to autoprompts yield similar effects in natural language inputs, suggesting that autoprompts emerge naturally from the way LMs process linguistic inputs in general.
Steering Prepositional Phrases in Language Models: A Case of with-headed Adjectival and Adverbial Complements in Gemma-2
Authors: Stefan Arnold; Rene Gröbner
Venue: EMNLP 2025 (Proceedings of the 8th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP)

Abstract: Language Models, when generating prepositional phrases, must often decide for whether their complements functions as an instrumental adjunct (describing the verb adverbially) or an attributive modifier (enriching the noun adjectivally), yet the internal mechanisms that resolve this split decision remain poorly understood. In this study, we conduct a targeted investigation into Gemma-2 to uncover and control the generation of prepositional complements. We assemble a prompt suite containing with-headed prepositional phrases whose contexts equally accommodate either an instrumental or attributive continuation, revealing a strong preference for an instrumental reading at a ratio of 3:4. To pinpoint individual attention heads that favor instrumental over attributive complements, we project activations into the vocabulary space. By scaling the value vector of a single attention head, we can shift the distribution of functional roles of complements, attenuating instruments to 33% while elevating attributes to 36%.
The Comparative Trap: Pairwise Comparisons Amplifies Biased Preferences of LLM Evaluators
Authors: Hawon Jeong; ChaeHun Park; Jimin Hong; Hojoon Lee; Jaegul Choo
Venue: EMNLP 2025 (Proceedings of the 8th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP)

Abstract: As large language models (LLMs) are increasingly used as evaluators for natural language generation tasks, ensuring unbiased assessments is essential. However, LLM evaluators often display biased preferences, such as favoring verbosity and authoritative tones.Our empirical analysis reveals that these biases are exacerbated in pairwise evaluation, where LLMs directly compare two outputs and easily prioritize superficial attributes. In contrast, pointwise evaluation, which assesses outputs independently, is less susceptible to such bias because each output is judged in isolation. To address the limitations of the pairwise evaluation, we introduce a novel evaluation method, PRePair, which integrates pointwise reasoning within a pairwise framework. PRePair effectively alleviates biased preference, improving performance on the adversarial benchmark (LLMBar) while outperforming pointwise evaluation on the standard benchmark (MT-Bench).
Not a nuisance but a useful heuristic: Outlier dimensions favor frequent tokens in language models
Authors: Iuri Macocco; Nora Graichen; Gemma Boleda; Marco Baroni
Venue: EMNLP 2025 (Proceedings of the 8th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP)

Abstract: We study last-layer outlier dimensions, i.e. dimensions that display extreme activations for the majority of inputs. We show that outlier dimensions arise in many different modern language models, and trace their function back to the heuristic of constantly predicting frequent words. We further show how a model can block this heuristic when it is not contextually appropriate, by assigning a counterbalancing weight mass to the remaining dimensions, and we investigate which model parameters boost outlier dimensions and when they arise during training. We conclude that outlier dimensions are a specialized mechanism discovered by many distinct models to implement a useful token prediction heuristic.
Language Dominance in Multilingual Large Language Models
Authors: Nadav Shani; Ali Basirat
Venue: EMNLP 2025 (Proceedings of the 8th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP)

Abstract: This paper investigates the language dominance hypothesis in multilingual large language models (LLMs), which posits that cross-lingual understanding is facilitated by an implicit translation into a dominant language seen more frequently during pretraining. We propose a novel approach to quantify how languages influence one another in a language model. By analyzing the hidden states across intermediate layers of language models, we model interactions between language-specific embedding spaces using Gaussian Mixture Models. Our results reveal only weak signs of language dominance in middle layers, affecting only a fraction of tokens. Our findings suggest that multilingual processing in LLMs is better explained by language-specific and shared representational spaces rather than internal translation into a single dominant language.
Interpreting Language Models Through Concept Descriptions: A Survey
Authors: Nils Feldhus; Laura Kopf
Venue: EMNLP 2025 (Proceedings of the 8th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP)

Abstract: Understanding the decision-making processes of neural networks is a central goal of mechanistic interpretability. In the context of Large Language Models (LLMs), this involves uncovering the underlying mechanisms and identifying the roles of individual model components such as neurons and attention heads, as well as model abstractions such as the learned sparse features extracted by Sparse Autoencoders (SAEs). A rapidly growing line of work tackles this challenge by using powerful generator models to produce open-vocabulary, natural language concept descriptions for these components. In this paper, we provide the first survey of the emerging field of concept descriptions for model components and abstractions. We chart the key methods for generating these descriptions, the evolving landscape of automated and human metrics for evaluating them, and the datasets that underpin this research. Our synthesis reveals a growing demand for more rigorous, causal evaluation. By outlining the state of the art and identifying key challenges, this survey provides a roadmap for future research toward making models more transparent.
Investigating ReLoRA: Effects on the Learning Dynamics of Small Language Models
Authors: Yuval Weiss; David Demitri Africa; Paula Buttery; Richard Diehl Martinez
Venue: EMNLP 2025 (Proceedings of the 8th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP)

Abstract: Parameter-efficient methods like LoRA have revolutionised large language model (LLM) fine-tuning. ReLoRA extends this idea to pretraining by repeatedly merging and reinitialising low-rank adapters, increasing cumulative rank while keeping updates cheap. This aligns well with observations that high-capacity models learn through locally low-rank trajectories that expand over time. By contrast, recent work suggests that small language models (SLMs) exhibit rank deficiencies and under-utilise their available dimensionality. This raises a natural question: can ReLoRA’s rank-expanding update rule steer SLMs toward healthier learning dynamics, mitigating rank bottlenecks in a capacity-constrained regime? We argue SLMs are an ideal testbed: they train quickly, enable controlled ablations, and make rank phenomena more measurable. We present the first systematic study of ReLoRA in SLMs (11M-66M parameters), evaluating both performance and learning dynamics. Across loss, Paloma perplexity, and BLiMP, we find that ReLoRA underperforms full-rank training, with gaps widening at larger scales. Analysis of proportional effective rank and condition numbers shows that ReLoRA amplifies existing rank deficiencies and induces ill-conditioned updates early in training. Our results suggest that while ReLoRA’s merge-and-restart strategy can expand ranks in larger models, it does not straightforwardly translate to capacity-limited SLMs, motivating adaptive-rank or hybrid-rank approaches for low-compute pretraining.
When LRP Diverges from Leave-One-Out in Transformers
Authors: Weiqiu You; Siqi Zeng; Yao-Hung Hubert Tsai; Makoto Yamada; Han Zhao
Venue: EMNLP 2025 (Proceedings of the 8th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP)

Abstract: Leave-One-Out (LOO) provides an intuitive measure of feature importance but is computationally prohibitive. While Layer-Wise Relevance Propagation (LRP) offers a potentially efficient alternative, its axiomatic soundness in modern Transformers remains under-examined. In this work, we first show that the bilinear propagation rules used in recent advances of AttnLRP violate implementation invariance. We prove this analytically and confirm it empirically in linear attention layers. Second, we also revisit CP-LRP as a diagnostic baseline and find that bypassing relevance propagation through the softmax layer—back-propagating relevance only through the value matrices—significantly improves alignment with LOO, particularly in the middle-to-late Transformer layers. Overall, our results suggest that (i) bilinear factorization sensitivity and (ii) softmax propagation error potentially jointly undermine LRP’s ability to approximate LOO in Transformers.
Understanding the Side Effects of Rank-One Knowledge Editing
Authors: Ryosuke Takahashi; Go Kamoda; Benjamin Heinzerling; Keisuke Sakaguchi; Kentaro Inui
Venue: EMNLP 2025 (Proceedings of the 8th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP)

Abstract: This study conducts a detailed analysis of the side effects of rank-one knowledge editing using language models with controlled knowledge. The analysis focuses on each element of knowledge triples (subject, relation, object) and examines two aspects: “knowledge that causes large side effects when edited” and “knowledge that is affected by the side effects.” Our findings suggest that editing knowledge with subjects that have relationships with numerous objects or are robustly embedded within the LM may trigger extensive side effects. Furthermore, we demonstrate that the similarity between relation vectors, the density of object vectors, and the distortion of knowledge representations are closely related to how susceptible knowledge is to editing influences. The findings of this research provide new insights into the mechanisms of side effects in LM knowledge editing and indicate specific directions for developing more effective and reliable knowledge editing methods.
Emergent Convergence in Multi-Agent LLM Annotation
Authors: Angelina Parfenova; Alexander Denzler; Jürgen Pfeffer
Venue: EMNLP 2025 (Proceedings of the 8th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP)

Abstract: Large language models (LLMs) are increasingly deployed in collaborative settings, yet little is known about how they coordinate when treated as black-box agents. We simulate 7,500 multi-agent, multi-round discussions in an inductive coding task, generating over 125,000 utterances that capture both final annotations and their interactional histories. We introduce process-level metrics—code stability, semantic self-consistency, and lexical confidence—alongside sentiment and convergence measures, to track coordination dynamics. To probe deeper alignment signals, we analyze the evolving geometry of output embeddings, showing that intrinsic dimensionality declines over rounds, suggesting semantic compression. The results reveal that LLM groups converge lexically and semantically, develop asymmetric influence patterns, and exhibit negotiation-like behaviors despite the absence of explicit role prompting. This work demonstrates how black-box interaction analysis can surface emergent coordination strategies, offering a scalable complement to internal probe-based interpretability methods.