ChatGPT may Pass the Bar Exam soon, but has a Long Way to Go for the LexGLUE benchmark

Following the hype around OpenAI's ChatGPT conversational agent, the last straw in the recent development of Large Language Models (LLMs) that demonstrate emergent unprecedented zero-shot capabilities, we audit the latest OpenAI's GPT-3.5 model, `gpt-3.5-turbo', the first available ChatGPT model, in the LexGLUE benchmark in a zero-shot fashion providing examples in a templated instruction-following format. The results indicate that ChatGPT achieves an average micro-F1 score of 47.6% across LexGLUE tasks, surpassing the baseline guessing rates. Notably, the model performs exceptionally well in some datasets, achieving micro-F1 scores of 62.8% and 70.2% in the ECtHR B and LEDGAR datasets, respectively. The code base and model predictions are available for review on https://github.com/coastalcph/zeroshot_lexglue.Comment: Working pape

Nano-metal oxides for elemental mercury removal from natural gas

Undoubtedly mercury pollution poses detrimental threats to the human health and environment. Global initiatives such as the Minamata Convention on Mercury (2017) and the United Nations Environment Programme (2018) have emphatically addressed the necessity to undertake drastic actions to reduce anthropogenic emissions. At the same time, the role of natural gas in mitigating greenhouse gas emissions and advancing renewable energy integration is certainly critical. It accounts for less than 25% of the world fuel consumption, with liquified natural gas (LNG) comprising around 10% of global gas trade. The intensive exploitation of low-cost shallow hydrocarbon reservoirs has directed exploration to deeper geological basins. However, drilling into deeper and hotter reservoirs increases the possibility of mercury contamination. This impacts on the industry sustainability from health and safety risk aspects due to corrosion and contamination of equipment, to catalyst poisoning and toxicity through emissions to the environment. In the past, catastrophic industrial events, such as the one occurred in Skikda LNG plant, Algeria in 1973 or the most recent 2004 New Year's Day Moomba gas plant explosion in South Australia, were attributed to mercury-induced corrosion. Elemental mercury (Hg(0)) is the prevalent species in natural gas streams and it is characterised by its low water solubility and high mobility in the atmosphere. According to the industry practice, the mercury concentration of the gas stream entering the cryogenic section, must be reduced to fewer than 10 ng/m3. Thus, fixed-beds of non-regenerative and regenerative materials, such as sulfur-impregnated carbons, supported metal sulfides/oxides and silver-impregnated zeolites, are utilized to remove mercury by means of amalgamation, chemical or physical adsorption as well as reactive absorption. Since the 1970's, when the effect of mercury on natural gas cryogenic processing plants was realized, numerous patents have been published. Nevertheless, the conventional materials are often problematic and alternative ones, are currently under research. These need to be efficient at ambient temperatures, regenerable at low temperatures, and effective at high-mercury concentration streams and reducing atmospheres. Given the extremely low mercury concentration specifications in LNG plants, the target efficiencies may as high as 99.9%, and when the efficiency drops below 99%, the sorbent may need to be replaced. Metal oxides possess high adsorption capacity, strong metal ion affinity and the ability to remove heavy metal traces with the possibility of recovery and reuse. Among them, manganese oxides are low cost, environmentally friendly and have been tested as sorbents/catalysts for the elimination of various gaseous pollutants including Hg(0) vapor. A particular crystal phase of MnO2, alpha-phase MnO2, consists of 2x2 (4.6x4.6 Å) tunnels constructed from double chains of octahedra [MnO6] with a variety of stabilizing cations (e.g. K+, Ba2+) being situated inside its tunnels. Its distinctive properties which are related to the tunnel cavity, moderately acidic sites and easy release of lattice oxygen have rendered it promising for sorption and catalytic applications. Furthermore, various studies have involved the incorporation of CeO2 into sorbent/catalyst compositions with the aim of enhancing the Hg(0) removal activity, usually at temperatures over 150 °C. This is due to its unique properties, which are related to the reversible Ce3+/Ce4+ redox pair, the surface acid-base properties, and the defects that primarily consist of oxygen vacancies. The target of this study was to develop a regenerable Hg(0) vapor composite sorbent based on nanostructured manganese and cerium oxides, for Hg(0) removal from natural gas streams at ambient conditions. The experimental work was divided into three parts. In the first part, different nanomorphologies (nanotubes, nanorods and nanowires) of  alpha-phase MnO2 were hydrothermally synthesized and evaluated for Hg(0) capture at CH4/N2 mixtures containing both CO2 and H2S. Alpha-phase MnO2 nanotubes manifested the highest uptake (1 wt%) and could be regenerated at only 250 °C. The enhanced activity of nanotubes was ascribed to the presence of abundant surface adsorbed oxygen species, that are believed to facilitate Hg0 adsorption. In the next part the aim was to improve the removal capacity of  alpha-phase MnO2 nanotubes by sustaining at the same time its regeneration ability. To achieve this, alpha-phase MnO2 nanotubes were decorated with CeO2 nanoparticles via a low-temperature hydrothermal method. The CeO2-saturated nanosorbent, which had a relative Ce/Mn atomic weight ratio of around 35%, exhibited a maximum Hg(0) uptake capacity exceeding 2 wt%, as determined from measurements of mercury breakthrough which corresponded to 99.5% Hg(0) removal efficiency. Its impressive activity was speculated to be related with the facile oxygen vacancy formation at alpha-phase MnO2 NTs as a result of CeO2 incorporation. Finally in the third part, the potential industrial application of the developed nanosorbent was demonstrated by coating a 3D-printed Ti-based support structure with the developed Mn-Ce nanocomposite. Engineered honeycomb-like structures are advantageous in gas separations because they can provide substantially uniform flow paths which reduce the pressure drop. The structured support was manufactured by a 3D-printing process (SLM) using as material source a titanium alloy, Ti-6Al-4V powder, which is attractive for application in oil and gas industry due to its corrosion resistance and high strength to weigh ratio. Despite the fact that the active phase, which was loaded on its surface by a simple dip-coating process, constituted less than 5 wt% of the total mass, the coated structure captured almost 100% Hg(0) vapor in the preliminary 7-h test. The future research plans concern the optimization of the coating process, the examination of alternatives synthetic routes to produce the developed Mn-Ce nanocomposite as well as the investigation of its tolerance against water vapor

An empirical study on cross-x transfer for legal judgment prediction

Cross-lingual transfer learning has proven useful in a variety of Natural Language Processing (NLP) tasks, but it is understudied in the context of legal NLP, and not at all in Legal Judgment Prediction (LJP). We explore transfer learning techniques on LJP using the trilingual Swiss-Judgment-Prediction dataset, including cases written in three languages. We find that cross-lingual transfer improves the overall results across languages, especially when we use adapter-based fine-tuning. Finally, we further improve the model's performance by augmenting the training dataset with machine-translated versions of the original documents, using a 3x larger training corpus. Further on, we perform an analysis exploring the effect of cross-domain and cross-regional transfer, i.e., train a model across domains (legal areas), or regions. We find that in both settings (legal areas, origin regions), models trained across all groups perform overall better, while they also have improved results in the worst-case scenarios. Finally, we report improved results when we ambitiously apply cross-jurisdiction transfer, where we further augment our dataset with Indian legal cases

On the Interplay between Fairness and Explainability

In order to build reliable and trustworthy NLP applications, models need to be both fair across different demographics and explainable. Usually these two objectives, fairness and explainability, are optimized and/or examined independently of each other. Instead, we argue that forthcoming, trustworthy NLP systems should consider both. In this work, we perform a first study to understand how they influence each other: do fair(er) models rely on more plausible rationales? and vice versa. To this end, we conduct experiments on two English multi-class text classification datasets, BIOS and ECtHR, that provide information on gender and nationality, respectively, as well as human-annotated rationales. We fine-tune pre-trained language models with several methods for (i) bias mitigation, which aims to improve fairness; (ii) rationale extraction, which aims to produce plausible explanations. We find that bias mitigation algorithms do not always lead to fairer models. Moreover, we discover that empirical fairness and explainability are orthogonal.Comment: 15 pages (incl Appendix), 4 figures, 8 table

Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark

In many jurisdictions, the excessive workload of courts leads to high delays. Suitable predictive AI models can assist legal professionals in their work, and thus enhance and speed up the process. So far, Legal Judgment Prediction (LJP) datasets have been released in English, French, and Chinese. We publicly release a multilingual (German, French, and Italian), diachronic (2000-2020) corpus of 85K cases from the Federal Supreme Court of Switzerland (FSCS). We evaluate state-of-the-art BERT-based methods including two variants of BERT that overcome the BERT input (text) length limitation (up to 512 tokens). Hierarchical BERT has the best performance (approx. 68-70% Macro-F1-Score in German and French). Furthermore, we study how several factors (canton of origin, year of publication, text length, legal area) affect performance. We release both the benchmark dataset and our code to accelerate future research and ensure reproducibility