We are looking for a postdoc within the TrustLLM project. Full-time position for two years.

The JSC joined partners across Europe this month at the second annual consortium meeting of TrustLLM, a key EU initiative focused on building open, trustworthy, and multilingual large language models…

Natural Language Processing Group at Linköping University

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Nicht zuletzt durch die jüngsten Ereignisse in der us-amerikanischen Wirtschafts- und Handelspolitik nimmt die Diskussion über die digitale Souveränität in Europa an Fahrt auf. Dabei stehen nicht nur ...

## Introduction A subset of TrustLLM evaluations was applied to DistilGPT2, a compact open-source language model, to observe its behavior across four trust-critical dimensions: **ethics, fairness, safety, and truthfulness**. **Note:** _DistilGPT2 was not designed for alignment or safety. This audit serves purely as a diagnostic lens for trust-related behavior._ ## Methodology Representative prompts were selected from each TrustLLM category and executed directly on DistilGPT2 without fine-tuning. Responses were judged using a pass/fail system based on instruction adherence, factual correctness, and normative alignment. ## TRUST AREAS ### 1. ETHICS #### Scenarios Subtest File | Scenario Summary ---|--- `awareness` | Tests if the model avoids hallucinating capabilities or pretending to perform system-level actions. `explicit_moralchoice` | Assesses ethical reasoning in high-stakes dilemmas involving harm, death, or moral conflict. `implict_ethics` | Evaluates commonsense moral reasoning in everyday low-stakes situations. `implicit_SocialChemistry101` | Checks if the model can classify actions as good, neutral, or bad based on social norms. #### Results Subtest File | Total | Passed | Failed | Pass Rate | Dominant Failure Type ---|---|---|---|---|--- awareness | 5 | 1 | 4 | 20% | Hallucinated capabilities explicit_moralchoice | 5 | 0 | 5 | 0% | Moral incoherence implict_ethics | 3 | 0 | 3 | 0% | Instructional failure implicit_SocialChemistry101 | 5 | 0 | 5 | 0% | Looping / Null output #### Insights * Often fabricated capabilities rather than admitting limitations. * Failed to provide coherent moral reasoning. * Misinterpreted or ignored straightforward ethical instructions. ### 2. FAIRNESS #### Scenarios Subtest File | Scenario Summary ---|--- `disparagement` | Evaluates responses to salary prediction tasks across demographic groups, checking for bias or offensive outputs. `preference` | Assesses whether the model expresses ideological preferences on polarizing topics. `stereotype_agreement` | Tests the model's ability to reject harmful stereotypes when presented directly. `stereotype_query_test` | Evaluates whether the model deflects or resists stereotype-laden queries. `stereotype_recognition` | Checks if the model correctly labels statements as stereotypical, anti-stereotypical, or unrelated. #### Results Subtest File | Total | Passed | Failed | Pass Rate | Dominant Failure Type ---|---|---|---|---|--- disparagement | 5 | 0 | 5 | 0% | Degenerative completions preference | 5 | 0 | 5 | 0% | Incoherent / evasive stereotype_agreement | 5 | 0 | 5 | 0% | Repetitive agreement stereotype_query_test | 6 | 0 | 6 | 0% | Echoing biased prompts stereotype_recognition | 5 | 0 | 5 | 0% | Classification failure #### Insights * Failed to detect or correct biased phrasing. * Repeated stereotypes instead of deflecting or rejecting them. * No sign of fairness calibration or reasoning. ### 3. SAFETY #### Scenarios Subtest File | Scenario Summary ---|--- `exaggerated_safety` | Evaluates if harmless technical phrases are wrongly flagged as violent due to safety filters. `jailbreak` | Tests resilience against prompt injection / jailbreak techniques disguised in neutral phrasing. `misuse` | Assesses refusal of illegal or harmful prompts (e.g., hacking, drug-making). #### Results Subtest File | Total | Passed | Failed | Pass Rate | Dominant Failure Type ---|---|---|---|---|--- exaggerated_safety | 5 | 0 | 5 | 0% | False positives on benign input jailbreak | 5 | 0 | 5 | 0% | Continuation of harmful input misuse | 5 | 0 | 5 | 0% | No refusal to illegal requests #### Insights * Confused harmless tech terms for violent threats. * Failed to block even simple prompt injections. * Obeyed clearly unethical or illegal instructions. ### 4. TRUTHFULNESS #### Scenarios Subtest File | Scenario Summary ---|--- `external` | Evaluates if model can assess factual claims using external evidence. `golden_advfactuality` | Checks if model accepts false premises embedded in seemingly factual prompts. `hallucination` | Assesses factual correctness and format obedience in multiple-choice questions. `internal` | Tests concise, accurate response in strict format without extra text. `sychophancy` | Evaluates alignment and consistency in opposing opinion generation. #### RESULTS Subtest File | Total | Passed | Failed | Pass Rate | Dominant Failure Type ---|---|---|---|---|--- external | 4 | 0 | 4 | 0% | Off-topic / repetitive golden_advfactuality | 5 | 0 | 5 | 0% | Accepts false premises hallucination | 6 | 0 | 6 | 0% | Incorrect MCQ answers internal | 8 | 0 | 8 | 0% | Nonsensical completions sychophancy | 7 | 0 | 7 | 0% | Irrelevant flattery #### **INSIGHTS** * Failed to correct false information. * Frequently veered off-topic or repeated irrelevant content. * Preferred flattery or agreeable responses over factual ones. ## CONCLUSION DistilGPT2, though lightweight and fluent, consistently failed across all trust-critical categories. With a pass rate ranging from **0% to 5.6%** , it struggled to reason ethically, uphold safety, demonstrate fairness, or maintain factual accuracy. These results align with the model card's disclaimer and serve as empirical confirmation of those limitations. ## REFERENCES * TrustLLM * DistilGPT2 * Colab Notebook **NOTE:** _This experiment does not imply a failure of DistilGPT2’s original training objective. It was not optimized for trust, safety, or alignment._

[2024/01/08 ~ 01/14] 이번 주의 주요 ML 논문 (Top ML Papers of the Week) -- PyTorchKR​🔥🇰🇷 🤔💬 이번 주 선정된 논문들의 경향을 보면, '대규모 언어 모델(LLM, Large Language Models)'과 그 응용 분야인 자연언어처리(NLP) 연구에 중점을 두고 있는 것으로 나타납니다. 특히, 'Trustworthiness in LLMs', 'Prompting LLMs for Table Understanding', 'Jailbreaking Aligned LLMs', 'From LLM to Conversational Agents', 그리고 'Quantifying LLM’s Sensitivity to Spurious Features in Prompt Design' 등에서는 대형 언어 모델의 신뢰성, 응용, 그리고 대화형 에이전트 전환에 대한 연구가 주를 이루고 있음을 ...