Sep 2026· Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence· 0 citations· 37 references
TL;DR
This work evaluates 70+ SLMs from 2023–2025 on five task-specific benchmarks and compares them with two popular LLMs, revealing key trade-offs between energy, performance, and model selection and highlighting the need for informed, task-aware model selection rather than size-driven choices.
Abstract
Large language models (LLMs) dominate both everyday and specialized applications, but their high computational demand, energy consumption, and privacy risks are increasingly critiqued. Small language models (SLMs) mitigate these drawbacks and are gaining momentum in scenarios where full LLM capabilities are not required, such as agents, industrial systems, or edge devices. Nevertheless, a systematic comparison of model capabilities, energy usage, and scaling behavior has not been conducted yet. We evaluate 70+ SLMs from 2023–2025 on five task-specific benchmarks and compare them with two popular LLMs, revealing key trade-offs between energy, performance, and model selection. Our findings challenge common assumptions: First, smaller models are not automatically more efficient, and energy increases do not guarantee performance gains. Second, newer SLMs show clear improvements in performance–energy trade-offs, though the progress begins to plateau. Last, the efficiency landscape forms a clear Pareto frontier: initial energy increases yield substantial gains, but the last percentage points of performance need orders of magnitude more energy. These results highlight diminishing returns of scaling and emphasize the need for informed, task-aware model selection rather than size-driven choices.
These results show that selecting lightweight LLMs by size, FLOPs, latency, or accuracy alone can select the wrong deployment candidate; PTME profiling exposes configurations that preserve useful accuracy at lower physical cost.
Tomohiro Harada, Enrique Alba, Gabriel Luque· arXiv.org· 0 citations
Due to their black-box nature, LLMs suffer from limited explain- ability and a lack of determinism. Their usage cost can also rise, particularly with repetitive tasks on large codebases. To mitigate this, we conduct a novel empirical study targeting three domain- specific languages for transformation rules, namely Comby, GritQL, and Ast-Grep. We evaluate three LLMs (GPT-5.4, GPT-oss-120B, and Llama3.1-8B) on six diverse datasets covering four software- evolution tasks: API misuse correction, program repair, API migra- tion, and language version migration. Our results provide evidence that transformation rule synthesis moves beyond proof-of-concept with strong frontier models. GPT-5.4 achieves consistently high rule applicability rates and produces transformations closest to the ground truth across most benchmarks. Smaller and open-weight GPT-oss-120B and Llama3.1-8B models remain effective for simpler, localized changes but struggle with complex migration scenarios. We also observe non-negligible generalizability through the usage of meta-variables and through a high reuse score in the first quartile of many datasets. Finally, when compared to the anti-unification algorithm, LLMs outperform it in correctness, but underperform in rule applicability. Overall, our results show great potential for LLMs to generate sound, correct, generalizable, and reusable rules.
Axel Allain, Aymeric Blot, D. Khelladi et al.· 1 citation
LangChoiceBench is introduced, a project-level code-generation benchmark for measuring Python preference, recommendation-implementation consistency, and language diversity, and it is found that Python remains heavily over-selected, recommendation-implementation consistency is low, and smaller open-weight models generally show stronger Python preference and lower language diversity.
Lukas Twist, Twm Stone, Helen Yannakoudakis et al.· 0 citations
Findings show that evidence-grounded orchestration can substantially improve the performance of collaborative SLMs for supporting interpretation of malware detonation reports.
: Large language models are increasingly being deployed in safety-critical domains, yet remain vulnerable to jailbreak attacks that circumvent safety alignments. This systematic review synthesizes empirical jailbreak research published between 2024 and 2025, using a PRISMA-guided search protocol, followed by BERTopic-based topic modeling. The analysis identifies eight main jailbreak categories: optimization-based, ge-netic/evolutionary, iterative refinement, semantic/persuasion-based, decomposition, context/generation-level, visual/encoding and fuzzing attacks, and characterizes their effectiveness, efficiency, and transferability across open-source and proprietary models, including Llama-2/3, Vicuna, GPT-3.5/4, Claude, Gemini, and DeepSeek-V3. Results show that simple configuration and context-level attacks can match the near-perfect attack success rates of sophisticated white-box optimization methods on models such as Llama-2, while requiring far fewer queries and no parameter access, highlighting a gap between research focus and practical threat severity. The review further identifies five recurring vulnerability mechanisms: representation-level gaps, execution-priority manipulation, semantic fragmentation, gradient-space exploitation and persuasion susceptibility, and documents family-specific vulnerability patterns, with open-source Llama-based models consistently more exposed than safety-enhanced architectures such as Claude. Diverse methods, uneven focus on models and publication bias limit how broadly results apply. Nonetheless, the review reveals that weaknesses in safety alignment persist across successive LLM generations, urging that effective defenses must address all eight attack categories rather than isolated techniques.
Meda Račaitytė, Hélder Bastos, R. Ribeiro et al.· International Conference on...· 0 citations
Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, yet their internal mechanisms remain largely opaque, making it difficult to understand, predict, or control their behavior. As LLMs are increasingly deployed in high-stakes settings, this lack of transparency raises serious concerns about reliability and safety. Mechanistic interpretability (MI) has emerged as a promising approach to address this challenge, seeking to reverse-engineer the internal computations of LLMs into human-understandable mechanisms, i.e., an approximate high-level algorithm that the LLM implements with a subset of its components (a circuit) to complete a certain language task or exhibit a certain behavior. This tutorial provides a comprehensive and up-to-date overview of LLM mechanism discovery, validation, and editing. We begin by introducing foundational concepts, including features, components, computational graphs, and circuits, along with key notation. We then examine mechanism discovery through four methodological families: causal mediation, attribution, sparse decomposition, and optimization-based approaches. Next, we turn to mechanism validation, covering methods for verifying proposed mechanisms and emerging standards for rigorous evaluation. Building on these foundations, we survey mechanistic editing techniques that leverage MI insights to modify behavior at varying granularity, from fine-grained representation-level steering to coarser circuit-level interventions. Lastly, we outline open challenges and future research directions, including scalability of interpretability methods, evaluation benchmarks for mechanistic circuits, and the integration of interpretability with training-time objectives, aiming to inspire continued progress in understanding and governing large language models.
Yinhan He, Wendy Zheng, Tianyi Zhao et al.· Proceedings of the 32nd ACM...· 0 citations
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