\rowcolors{2}{gray!25}{white}
\begin{tabular}{p{.25\linewidth} p{.4\linewidth} p{\linewidth}}
    \toprule
    \bf	Team	 \&	\bf	Paper &	\bf	Resources &	\bf	Overview		\\
    \midrule
    AI Blues	     	& \multicolumn{2}{c}{\it \small (No report)} \\
    AILS-NTUA\newline   	\citet{grigoriadou-EtAl:2024:SemEval2024}	& SHROOM datasets; Vectara model.& Fine-tuned models and voting classifier. \\
    Alejandro Mosquera 	& SHROOM datasets; COMET, Vectara, LaBSE, GPT35 and GPT4 models. & Ensemble of publicly available models. Logistic Regression was used as final scoring model. \\
    AlphaIntellect	 \newline 	\citet{choudhury-EtAl:2024:SemEval2024}	& SHROOM dataset, SBERT & Fully-connected neural network classifiers with SBERT embeddings as input. \\
    AMEX AI LABS	  		& SHROOM datasets; Vectara and OpenChat models. & Ensemble of LLM (using Openchat) zero shot and few shot with Vectara cross encoder based scores. \\
    Atresa	         		& \multicolumn{2}{c}{\it \small (No report)} \\
    Bolaca		\newline  \citet{rsener-wei-vandici:2024:SemEval2024}		& SHROOM dataset, SBERT & Logistic regression and feed-forward classifier trained on SBERT embeddings\\
    BrainLlama\newline   \citet{siino:2024:SemEval20243} & LLaMA model. & Prompt-based approach with LLaMA.	\\
    BruceW	        	& \multicolumn{2}{c}{\it \small (No report)}\\
    Byun \newline    \citet{byun:2024:SemEval2024}	& SHROOM dataset, data augmentation, RoBERTa & Finetuned a BERT or RoBERTa model with a softmax layer to output the probability of hallucinated text. Finetuning data is the labelled SHROOM data augmented with data points constructed by replacing words with synonyms.\\
    CAISA	         		& \multicolumn{2}{c}{\it \small (No report)}\\
    Compos Mentis \newline  \citet{das-srihari:2024:SemEval2024}	& HalluEval dataset; Mistral 7B instruct model. & Ensemble of several role-based LLMs, which were either fine-tuned on hallucination data or role-based prompting.  \\
    daixiang	      			& \multicolumn{2}{c}{\it \small (No report)}\\
    deema	          		& \multicolumn{2}{c}{\it \small (No report)}\\
    DeepPavlov	 \newline  \citet{belikova-kosenko:2024:SemEval2024}	& SHROOM dataset; OpenChat, DeBERTa, RoBERTa and T5 models. & Ensemble of several pretrained Transformer-based models to get features for validation and test data of SHROOM dataset and trained a boosting-based meta-model on top. \\
    DUTh	\newline  \citet{iordanidou-maslaris-arampatzis:2024:SemEval2024}	& SHROOM, LaBSE, T5, DistilUSE & Using pre-trained LLMs and classifiers\\
    HalluSafe 	\newline  \citet{rahimi-EtAl:2024:SemEval20241}	&  SHROOM, labeled 3000 samples of the training data	&  Fine-tuned a DeBERTa-v3-large \\
    Halu-NLP	\newline  \citet{mehta-EtAl:2024:SemEval2024}	& SHROOM datasets; GPT, SelfCheckGPT and Vectara models. & Prompts and GroupCheckGPT. NB: due to a team name change, this team is also referred to as GroupCheckGPT by some participants. \\
    HaRMoNEE 	\newline  \citet{obiso-tu-pustejovsky:2024:SemEval2024} & SHROOM, SNLI, MNLI and PAWS datasets; Vectara and GPT4 models. & Highest results obtained with zero-shot prompting in the model-aware track; pretraining on NLI and PAWS followed by finetuning on the model-agnostic track. \\
    HIT-MI\&T Lab	\newline  \citet{liu-EtAl:2024:SemEval2024}	& SHROOM with training dataset  labeled using GPT-4; DeBERTaV3, InternLM2, SBERT, and UniEval. &   %Furthermore, within the model-aware track, the entropy and probability of the forecasted tokens are computed for both DM and MT tasks. In the case of the PG task, we calculate the consistency based on the outcomes of multiple model invocations.
    Fine-tune the DeBERTaV3 and InternLM2 models, and call the SBERT and UniEval models to select the optimal threshold usinf SHROOM \& syntheticaly labeled data. The system obtians the final results by combining the prediction results of each model.\\
    IRIT-Berger-Levrault	\newline  \citet{bendahman-EtAl:2024:SemEval2024}	&SHROOM datasets; Sentence-t5, BGE, e5 models. & Computes the cosine similarity of sentence embeddings and classify based on an empirical threshold value. \\
    Maha Bhaashya	\newline  \citet{bhamidipati-EtAl:2024:SemEval2024}	& DeBERTa models. & Zero shot inference, pretrained cross encoder model  \\
    MALTO	\newline  \citet{borra-EtAl:2024:SemEval2024} & SHROOM model-agnostic dataset, DeBERTa pretrained and finetuned on MNLI, SOLAR-10.7B quantized from TheBloke (for synthetic data generation) & Encoder and classifier, fine-tuned in various ways (including with synthetic data)	\\
    MARiA	\newline  \citet{sanayei-EtAl:2024:SemEval2024}	& SHROOM dataset, SBERT, bart-large-mnli, Mixtral & Three approaches: (1) Cosine similarity of SBERT embeddings between source-hypothesis and source-target pairs; (2) NLI classification using bart-large-mnli model; and (3) Mixtral prompting. Only the Mixtral results were submitted. \\
    Noot Noot	\newline  \citet{bahad-bhaskar-krishnamurthy:2024:SemEval20243}	& SHROOM dataset; Mixtral and RoBERTa models. &  Mixtral prompting and  RoBERTa finetuning. \\
    NU-RU	\newline  \citet{markchom-jung-liang:2024:SemEval2024} & SHROOM, GPT-3.5, Sentence Transformers  &  Tried two approaches: (1) hypothesis-target cosine similarity, using a threshold value to determine whether the hypothesis is a hallucation. (2) SelfCheckGPT with a customized prompt for each NLG task, designed to assess its coherence with the provided source and target. Each prompt is iterated through the GPT-3.5 model five times, and the final label is determined by the majority response.  \\
    octavianB	\newline  \citet{brodoceanu:2024:SemEval2024}	& RoBERTa & Used a pretrained model (roberta-large-openai-detector) that has been trained to distinguish between text generated by LLMs and text written by humans.\\
    OPDAI	\newline  \citet{chen-EtAl:2024:SemEval20241}	& SHROOM, Mistral-7B-Instruct-v0.2, self constructed training data & Supervised fine-tuning over synthetically constructed weakly supervised training data. \\
    Pollice Verso	\newline  \citet{kobs-pfister-hotho:2024:SemEval2024}	& Mistral2, LLaMa2, Phi2 and Zephyr models; uses SHROOM train set for prompt optimization. & Ensembling over the output logits of prompt-based LLMs (mistral, llama etc) after automatically optimizing their prompts ("OPRO").\\
    SHROOM-INDElab	\newline  \citet{allen-polat-groth:2024:SemEval2024}	& SHROOM dataset; GPT 3.5 and GPT 4 models. & In-context learning with role-play and automatic prompt generation in a few-shot classfier, using a closed-source LLM. \\
    SibNN			& SHROOM datasets; XLM-RoBERTa model. & Fine-tunes a self-adaptive hierarchical variant of XLM-RoBERTa-XL twice: first as an embedder (in a few-shot mode), then as a binary classifier. More details at \newline \url{https://huggingface.co/bond005/xlm-roberta-xl-hallucination-detector}. \\
    silk\_road			& SHROOM datasets; Vectara model. & Fine-tunes an off-the-shelf Cross-Encoder hallucination evaluation model. \\
    Skoltech			& \multicolumn{2}{c}{\it \small (No report)}\\
    SLPL SHROOM	\newline  \citet{fallah-EtAl:2024:SemEval2024}	& SHROOM datasets; LaBSE, DeBERTa, Zephyr, Mistral and Llama2 models. & Using two LLMs to classify and explain their decision and another LLM to judge and decide based on those explanations. \\
    SmurfCat	\newline  \citet{rykov-EtAl:2024:SemEval2024}	& SHROOM (synthetically augmented), QQP and PAWS datasets; E5, T5, Vectara models. & Fine-tuning of e5-mistral-7b-instruct using synthetic data collected with LLaMA2-7B adapters trained to produce data with and without hallucinations. However, there are two other systems: one works as a voting ensemble of multiple LLMs, and another uses the Mutual Implication Score architecture. \\
    Team CentreBack			& SHROOM dataset; DeBERTa model. & Uses an off-the-shelf library (SelfCheckGPT's SelfCheckNLI function) to calculate contradiction scores on a small labeled test set and then defined a threshold for hallucination. \\
    TU Wien	\newline  \citet{arzt-EtAl:2024:SemEval2024} & SHROOM dataset; Vectara model. & Model-aware track best submissions uses a Vectara hallucination detection model finetuned on the validation set. The best model-agnostic track submission is a meta-model that utilizes linear regression and is trained on features that correspond to probabilities predicted by individual systems we implemented. 	\\
    UCC-NLP		& SHROOM dataset; GPT-3.5 and Vectara models. & Uses BertScore and GPT-3.5 to create synthetic labels and fine-tune a Vectara LLM. \\
    UMUTeam	\newline  \citet{pan-EtAl:2024:SemEval20241}	& SHROOM dataset; TULU-DPO model. & Zero-shot approach\\
    ustc\_xsong			& \multicolumn{2}{c}{\it \small (No report)} \\
    zhuming			& \multicolumn{2}{c}{\it \small (No report)} \\
    0x.Yuan		& Mistral, Mixtral, LLaMA, Falcon, WizardLM and Capybara models.  & Zero-shot prompt engineering. Expects most LLMs will have different hallucination patterns, and tests whether ensembling can mitigate this. \\
    \bottomrule
\end{tabular}

