Fetching the paper…
Reading the bibliography…
Preference learning is a central component for aligning current LLMs, but this process can be vulnerable to data poisoning attacks.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
Gu, T., Dolan-Gavitt, B., and Garg, S · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
Earlier work this paper cites.
A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings
Artetxe, M., Labaka, G., and Agirre, E · 2018
Earlier work this paper cites.
Parameter-efficient transfer learning for nlp
Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., de Laroussilhe, Q., Gesmundo, A., Attariyan, M., and Gelly, S · 2019
Earlier work this paper cites.
Universal adversarial triggers for attacking and analyzing NLP
Wallace, E., Feng, S., Kandpal, N., Gardner, M., and Singh, S · 2019
Earlier work this paper cites.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
Earlier work this paper cites.
Poison attacks against text datasets with conditional adversarially regularized autoencoder
Chan, A., Tay, Y., Ong, Y.-S., and Zhang, A · 2020
Earlier work this paper cites.
On the dangers of stochastic parrots: Can language models be too big?
Bender, E. M., Gebru, T., McMillan-Major, A., and Shmitchell, S · 2021
Earlier work this paper cites.
Badnl: Backdoor attacks against nlp models with semantic-preserving improvements
Chen, X., Salem, A., Chen, D., Backes, M., Ma, S., Shen, Q., Wu, Z., and Zhang, Y · 2021
Earlier work this paper cites.
Hidden killer: Invisible textual backdoor attacks with syntactic trigger
Qi, F., Li, M., Chen, Y., Zhang, Z., Liu, Z., Wang, Y., and Sun, M · 2021
Earlier work this paper cites.
Concealed data poisoning attacks on NLP models
Wallace, E., Zhao, T., Feng, S., and Singh, S · 2021
Earlier work this paper cites.
Rethinking stealthiness of backdoor attack against NLP models
Yang, W., Lin, Y., Li, P., Zhou, J., and Sun, X · 2021
Earlier work this paper cites.
Training a helpful and harmless assistant with reinforcement learning from human feedback
Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., Drain, D., Fort, S., Ganguli, D., Henighan, T. J., Joseph, N., Kadavath, S., Kernion, J., Conerly, T., El-Showk, S., Elhage, N., Hatfield-Dodds, Z., Hernandez, D., Hume, T., Johnston, S., Kravec, S., Lovitt, L., Nanda, N., Olsson, C., Amodei, D., Brown, T. B., Clark, J., McCandlish, S., Olah, C., Mann, B., and Kaplan, J · 2022
Earlier work this paper cites.
Scaling instruction-finetuned language models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, E., Wang, X., Dehghani, M., Brahma, S., Webson, A., Gu, S. S., Dai, Z., Suzgun, M., Chen, X., Chowdhery, A., Valter, D., Narang, S., Mishra, G., Yu, A. W., Zhao, V., Huang, Y., Dai, A. M., Yu, H., Petrov, S., hsin Chi, E. H., Dean, J., Devlin, J., Roberts, A., Zhou, D., Le, Q. V., and Wei, J · 2022
Earlier work this paper cites.
Triggerless backdoor attack for NLP tasks with clean labels
Gan, L., Li, J., Zhang, T., Li, X., Meng, Y., Wu, F., Yang, Y., Guo, S., and Fan, C · 2022
Earlier work this paper cites.
LoRA: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2022
Earlier work this paper cites.
Large language models struggle to learn long-tail knowledge
Kandpal, N., Deng, H., Roberts, A., Wallace, E., and Raffel, C · 2022
Earlier work this paper cites.
Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
Liu, H., Tam, D., Muqeeth, M., Mohta, J., Huang, T., Bansal, M., and Raffel, C · 2022
Earlier work this paper cites.
TimeLMs: Diachronic language models from Twitter
Loureiro, D., Barbieri, F., Neves, L., Espinosa Anke, L., and Camacho-collados, J · 2022
Earlier work this paper cites.
Rethinking the role of demonstrations: What makes in-context learning work?
Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., and Zettlemoyer, L · 2022
Earlier work this paper cites.
Cross-task generalization via natural language crowdsourcing instructions
Mishra, S., Khashabi, D., Baral, C., and Hajishirzi, H · 2022
Earlier work this paper cites.
Chatgpt: Optimizing language models for dialogue, 2022
OpenAI · 2022
Earlier work this paper cites.
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Gray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., and Lowe, R · 2022
Earlier work this paper cites.
Exploring the universal vulnerability of prompt-based learning paradigm
Xu, L., Chen, Y., Cui, G., Gao, H., and Liu, Z · 2022
Earlier work this paper cites.
Fine-mixing: Mitigating backdoors in fine-tuned language models
Zhang, Z., Lyu, L., Ma, X., Wang, C., and Sun, X · 2022
Earlier work this paper cites.
A general theoretical paradigm to understand learning from human preferences
Azar, M. G., Rowland, M., Piot, B., Guo, D., Calandriello, D., Valko, M., and Munos, R · 2023
Earlier work this paper cites.
Pythia: a suite for analyzing large language models across training and scaling
Biderman, S., Schoelkopf, H., Anthony, Q., Bradley, H., O’Brien, K., Hallahan, E., Khan, M. A., Purohit, S., Prashanth, U. S., Raff, E., Skowron, A., Sutawika, L., and Van Der Wal, O · 2023
Cited alongside, same era.
Backdoor learning on sequence to sequence models
Chen, L., Cheng, M., and Huang, H · 2023
Cited alongside, same era.
Chatgpt goes to law school
Choi, J. H., Hickman, K. E., Monahan, A. B., and Schwarcz, D. B · 2023
Cited alongside, same era.
Alpacafarm: A simulation framework for methods that learn from human feedback
Dubois, Y., Li, X., Taori, R., Zhang, T., Gulrajani, I., Ba, J., Guestrin, C., Liang, P., and Hashimoto, T · 2023
Cited alongside, same era.
Bias and fairness in large language models: A survey
Gallegos, I. O., Rossi, R. A., Barrow, J., Tanjim, M. M., Kim, S., Dernoncourt, F., Yu, T., Zhang, R., and Ahmed, N · 2023
Poisoning web-scale training datasets is practical
Carlini, N., Jagielski, M., Choquette-Choo, C. A., Paleka, D., Pearce, W., Anderson, H., Terzis, A., Thomas, K., and Tramèr, F · 2024
Closest in time.
Defending against unforeseen failure modes with latent adversarial training
Casper, S., Schulze, L., Patel, O., and Hadfield-Menell, D · 2024
Closest in time.
Trojanrag: Retrieval-augmented generation can be backdoor driver in large language models
Cheng, P., Ding, Y., Ju, T., Wu, Z., Du, W., Yi, P., Zhang, Z., and Liu, G · 2024
Closest in time.
Provably robust DPO: Aligning language models with noisy feedback
Chowdhury, S. R., Kini, A., and Natarajan, N · 2024
Closest in time.
Ultrafeedback: Boosting language models with high-quality feedback, 2024
Cui, G., Yuan, L., Ding, N., Yao, G., Zhu, W., Ni, Y., Xie, G., Liu, Z., and Sun, M · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Gemini: A family of highly capable multimodal models
Google, G. T · 2023
Cited alongside, same era.
Gunasekar, S., Zhang, Y., Aneja, J., Mendes, C. C. T., Giorno, A. D., Gopi, S., Javaheripi, M., Kauffmann, P., de Rosa, G., Saarikivi, O., Salim, A., Shah, S., Behl, H. S., Wang, X., Bubeck, S., Eldan, R., Kalai, A. T., Lee, Y. T., and Li, Y.-F · 2023
Cited alongside, same era.
He, K., Mao, R., Lin, Q., Ruan, Y., Lan, X., Feng, M., and Cambria, E · 2023
Cited alongside, same era.
Camels in a changing climate: Enhancing lm adaptation with tulu 2
Ivison, H., Wang, Y., Pyatkin, V., Lambert, N., Peters, M. E., Dasigi, P., Jang, J., Wadden, D., Smith, N. A., Beltagy, I., and Hajishirzi, H · 2023
Cited alongside, same era.
Beavertails: Towards improved safety alignment of LLM via a human-preference dataset
Ji, J., Liu, M., Dai, J., Pan, X., Zhang, C., Bian, C., Chen, B., Sun, R., Wang, Y., and Yang, Y · 2023
Cited alongside, same era.
HaluEval: A large-scale hallucination evaluation benchmark for large language models
Li, J., Cheng, X., Zhao, X., Nie, J.-Y., and Wen, J.-R · 2023
Cited alongside, same era.
GPT-4 technical report
OpenAI · 2023
Cited alongside, same era.
Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., et al · 2024
Closest in time.
Olmo: Accelerating the science of language models
Groeneveld, D., Beltagy, I., Walsh, P., Bhagia, A., Kinney, R., Tafjord, O., Jha, A. H., Ivison, H., Magnusson, I., Wang, Y., et al · 2024
Closest in time.
Exploring backdoor vulnerabilities of chat models
Hao, Y., Yang, W., and Lin, Y · 2024
Closest in time.
Sleeper agents: Training deceptive llms that persist through safety training
Hubinger, E., Denison, C., Mu, J., Lambert, M., Tong, M., MacDiarmid, M., Lanham, T., Ziegler, D. M., Maxwell, T., Cheng, N., et al · 2024
Closest in time.
Superfiltering: Weak-to-strong data filtering for fast instruction-tuning
Li, M., Zhang, Y., He, S., Li, Z., Zhao, H., Wang, J., Cheng, N., and Zhou, T · 2024
Closest in time.
SimPO: Simple preference optimization with a reference-free reward
Meng, Y., Xia, M., and Chen, D · 2024
Closest in time.
Is poisoning a real threat to llm alignment? maybe more so than you think
Pathmanathan, P., Chakraborty, S., Liu, X., Liang, Y., and Huang, F · 2024
Closest in time.
Learning to poison large language models during instruction tuning
Qiang, Y., Zhou, X., Zade, S. Z., Roshani, M. A., Zytko, D., and Zhu, D · 2024
Closest in time.
Universal jailbreak backdoors from poisoned human feedback
Rando, J. and Tramèr, F · 2024
Closest in time.
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Reid, M., Savinov, N., Teplyashin, D., Lepikhin, D., Lillicrap, T. P., Alayrac, J.-B., Soricut, R., Lazaridou, A., Firat, O., Schrittwieser, J., Antonoglou, I., Anil, R., Borgeaud, S., Dai, A. M., Millican, K., Dyer, E., Glaese, M., Sottiaux, T., Lee, B., Viola, F., Reynolds, M., Xu, Y., Molloy, J., Chen, J., Isard, M., Barham, P., Hennigan, T., McIlroy, R., Johnson, M., Schalkwyk, J., Collins, E., Rutherford, E., Moreira, E., Ayoub, K. W., Goel, M., Meyer, C., Thornton, G., Yang, Z., Michalewski, H., Abbas, Z., Schucher, N., Anand, A., Ives, R., Keeling, J., Lenc, K., Haykal, S., Shakeri, S., Shyam, P., Chowdhery, A., Ring, R., Spencer, S., Sezener, E., Vilnis, L., Chang, O., Morioka, N., Tucker, G., Zheng, C., Woodman, O., Attaluri, N., Kociský, T., Eltyshev, E., Chen, X., Chung, T., Selo, V., Brahma, S., Georgiev, P., Slone, A., Zhu, Z., Lottes, J., Qiao, S., Caine, B., Riedel, S., Tomala, A., Chadwick, M., Love, J. C., Choy, P., Mittal, S., Houlsby, N., Tang, Y., Lamm, M., Bai, L., Zhang, Q., He, L., Cheng, Y., Humphreys, P., Li, Y., Brin, S., Cassirer, A., Miao, Y.-Q., Zilka, L., Tobin, T., Xu, K., Proleev, L., Sohn, D., Magni, A., Hendricks, L. A., Gao, I., Ontan’on, S., Bunyan, O., Byrd, N., Sharma, A., Zhang, B., Pinto, M., Sinha, R., Mehta, H., Jia, D., Caelles, S., Webson, A., Morris, A., Roelofs, B., Ding, Y., Strudel, R., Xiong, X., Ritter, M., Dehghani, M., Chaabouni, R., Karmarkar, A., Lai, G., Mentzer, F., Xu, B., Li, Y., Zhang, Y., Paine, T. L., Goldin, A., Neyshabur, B., Baumli, K., Levskaya, A., Laskin, M., Jia, W., Rae, J. W., Xiao, K., He, A., Giordano, S., Yagati, L., Lespiau, J.-B., Natsev, P., Ganapathy, S., Liu, F., Martins, D., Chen, N., Xu, Y., Barnes, M., May, R., Vezer, A., Oh, J., Franko, K., Bridgers, S., Zhao, R., Wu, B., Mustafa, B., Sechrist, S., Parisotto, E., Pillai, T. S., Larkin, C., Gu, C., Sorokin, C., Krikun, M., Guseynov, A., Landon, J., Datta, R., Pritzel, A., Thacker, P., Yang, F., Hui, K., Hauth, A., Yeh, C.-K., Barker, D., Mao-Jones, J., Austin, S., Sheahan, H., Schuh, P., Svensson, J., Jain, R., Ramasesh, V. V., Briukhov, A., Chung, D.-W., von Glehn, T., Butterfield, C., Jhakra, P., Wiethoff, M., Frye, J., Grimstad, J., Changpinyo, B., Lan, C. L., Bortsova, A., Wu, Y., Voigtlaender, P., Sainath, T. N., Smith, C., Hawkins, W., Cao, K., Besley, J., Srinivasan, S., Omernick, M., Gaffney, C., de Castro Surita, G., Burnell, R., Damoc, B., Ahn, J., Brock, A., Pajarskas, M., Petrushkina, A., Noury, S., Blanco, L., Swersky, K., Ahuja, A., Avrahami, T., Misra, V., de Liedekerke, R., Iinuma, M., Polozov, A., York, S., van den Driessche, G., Michel, P., Chiu, J., Blevins, R., Gleicher, Z., Recasens, A., Rrustemi, A., Gribovskaya, E., Roy, A., Gworek, W., Arnold, S. M. R., Lee, L., Lee-Thorp, J., Maggioni, M., Piqueras, E., Badola, K., Vikram, S., Gonzalez, L., Baddepudi, A., Senter, E., Devlin, J., Qin, J., Azzam, M., Trebacz, M., Polacek, M., Krishnakumar, K., yiin Chang, S., Tung, M., Penchev, I., Joshi, R., Olszewska, K., Muir, C., Wirth, M., Hartman, A. J., Newlan, J., Kashem, S., Bolina, V., Dabir, E., van Amersfoort, J. R., Ahmed, Z., Cobon-Kerr, J., Kamath, A. B., Hrafnkelsson, A. M., Hou, L., Mackinnon, I., Frechette, A., Noland, E., Si, X., Taropa, E., Li, D., Crone, P., Gulati, A., Cevey, S., Adler, J., Ma, A., Silver, D., Tokumine, S., Powell, R., Lee, S., Chang, M. B., Hassan, S., Mincu, D., Yang, A., Levine, N., Brennan, J., Wang, M., Hodkinson, S., Zhao, J., Lipschultz, J., Pope, A., Chang, M. B., Li, C., Shafey, L. E., Paganini, M., Douglas, S., Bohnet, B., Pardo, F., Odoom, S., Rosca, M., dos Santos, C. N., Soparkar, K., Guez, A., Hudson, T., Hansen, S., Asawaroengchai, C., Addanki, R., Yu, T., Stokowiec, W., Khan, M., Gilmer, J., Lee, J., Bostock, C. G., Rong, K., Caton, J., Pejman, P., Pavetic, F., Brown, G., Sharma, V., Luvci’c, M., Samuel, R., Djolonga, J., Mandhane, A., Sjosund, L. L., Buchatskaya, E., White, E., Clay, N., Jiang, J., Lim, H., Hemsley, R., Labanowski, J., Cao, N. D., Steiner, D., Hashemi, S. H., Austin, J., Gergely, A., Blyth, T., Stanton, J., Shivakumar, K., Siddhant, A., Andreassen, A., Araya, C. L., Sethi, N., Shivanna, R., Hand, S., Bapna, A., Khodaei, A., Miech, A., Tanzer, G., Swing, A., Thakoor, S., Pan, Z., Nado, Z., Winkler, S., Yu, D., Saleh, M., Maggiore, L., Barr, I., Giang, M., Kagohara, T., Danihelka, I., Marathe, A., Feinberg, V., Elhawaty, M., Ghelani, N., Horgan, D., Miller, H., Walker, L., Tanburn, R., Tariq, M., Shrivastava, D., Xia, F., Chiu, C.-C., Ashwood, Z. C., Baatarsukh, K., Samangooei, S., Alcober, F., Stjerngren, A., Komarek, P., Tsihlas, K., Boral, A., Comanescu, R., Chen, J., Liu, R., Bloxwich, D., Chen, C., Sun, Y., Feng, F., Mauger, M., Dotiwalla, X., Hellendoorn, V., Sharman, M., Zheng, I., Haridasan, K., Barth-Maron, G., Swanson, C., Rogozi’nska, D., Andreev, A., Rubenstein, P. K., Sang, R., Hurt, D., Elsayed, G., Wang, R., Lacey, D., Ili’c, A., Zhao, Y., Aroyo, L., Iwuanyanwu, C., Nikolaev, V., Lakshminarayanan, B., Jazayeri, S., Kaufman, R. L., Varadarajan, M., Tekur, C., Fritz, D., Khalman, M., Reitter, D., Dasgupta, K., Sarcar, S., Ornduff, T., Snaider, J., Huot, F., Jia, J., Kemp, R., Trdin, N., Vijayakumar, A., Kim, L., Angermueller, C., Lao, L., Liu, T., Zhang, H., Engel, D., Greene, S., White, A., Austin, J., Taylor, L., Ashraf, S., Liu, D., Georgaki, M., Cai, I., Kulizhskaya, Y., Goenka, S., Saeta, B., Vodrahalli, K., Frank, C., de Cesare, D., Robenek, B., Richardson, H., Alnahlawi, M., Yew, C., Ponnapalli, P., Tagliasacchi, M., Korchemniy, A., Kim, Y., Li, D., Rosgen, B., Levin, K., Wiesner, J., Banzal, P., Srinivasan, P., Yu, H., cCauglar Unlu, Reid, D., Tung, Z., Finchelstein, D. F., Kumar, R., Elisseeff, A., Huang, J., Zhang, M., Zhu, R., Aguilar, R., Gim’enez, M., Xia, J., Dousse, O., Gierke, W., Yeganeh, S. H., Yates, D., Jalan, K., Li, L., Latorre-Chimoto, E., Nguyen, D. D., Durden, K., Kallakuri, P., Liu, Y., Johnson, M., Tsai, T., Talbert, A., Liu, J., Neitz, A., Elkind, C., Selvi, M., Jasarevic, M., Soares, L. B., Cui, A., Wang, P., Wang, A. W., Ye, X., Kallarackal, K., Loher, L., Lam, H., Broder, J., Holtmann-Rice, D. N., Martin, N., Ramadhana, B., Toyama, D., Shukla, M., Basu, S., Mohan, A., Fernando, N., Fiedel, N., Paterson, K., Li, H., Garg, A., Park, J., Choi, D., Wu, D., Singh, S., Zhang, Z., Globerson, A., Yu, L., Carpenter, J., de Chaumont Quitry, F., Radebaugh, C., Lin, C.-C., Tudor, A., Shroff, P., Garmon, D., Du, D., Vats, N., Lu, H., Iqbal, S., Yakubovich, A., Tripuraneni, N., Manyika, J., Qureshi, H., Hua, N., Ngani, C., Raad, M. A., Forbes, H., Bulanova, A., Stanway, J., Sundararajan, M., Ungureanu, V., Bishop, C., Li, Y., Venkatraman, B., Li, B., Thornton, C., Scellato, S., Gupta, N., Wang, Y., Tenney, I., Wu, X., Shenoy, A., Carvajal, G., Wright, D. G., Bariach, B., Xiao, Z., Hawkins, P., Dalmia, S., Farabet, C., Valenzuela, P., Yuan, Q., Welty, C. A., Agarwal, A., Chen, M., Kim, W., Hulse, B., Dukkipati, N., Paszke, A., Bolt, A., Davoodi, E., Choo, K., Beattie, J., Prendki, J., Vashisht, H., Santamaria-Fernandez, R., Cobo, L. C., Wilkiewicz, J., Madras, D., Elqursh, A., Uy, G., Ramirez, K., Harvey, M., Liechty, T., Zen, H., Seibert, J., Hu, C. H., Khorlin, A. Y., Le, M., Aharoni, A., Li, M., Wang, L., Kumar, S., Lince, A., Casagrande, N., Hoover, J., Badawy, D. E., Soergel, D., Vnukov, D., Miecnikowski, M., Sima, J., Koop, A., Kumar, P., Sellam, T., Vlasic, D., Daruki, S., Shabat, N., Zhang, J., Su, G., Krishna, K., Zhang, J., Liu, J., Sun, Y., Palmer, E., Ghaffarkhah, A., Xiong, X., Cotruta, V., Fink, M., Dixon, L., Sreevatsa, A., Goedeckemeyer, A., Dimitriev, A., Jafari, M., Crocker, R., Fitzgerald, N. A., Kumar, A., Ghemawat, S., Philips, I., Liu, F., Liang, Y., Sterneck, R., Repina, A., Wu, M., Knight, L., Georgiev, M., Lee, H., Askham, H., Chakladar, A., Louis, A., Crous, C., Cate, H., Petrova, D., Quinn, M., Owusu-Afriyie, D., Singhal, A., Wei, N., Kim, S., Vincent, D., Nasr, M., Choquette-Choo, C. A., Tojo, R., Lu, S., de Las Casas, D., Cheng, Y., Bolukbasi, T., Lee, K., Fatehi, S., Ananthanarayanan, R., Patel, M., Kaed, C. E., Li, J., Sygnowski, J., Belle, S. R., Chen, Z., Konzelmann, J., Poder, S., Garg, R., Koverkathu, V., Brown, A., Dyer, C., Liu, R., Nova, A., Xu, J., Petrov, S., Hassabis, D., Kavukcuoglu, K., Dean, J., and Vinyals, O · 2024
Closest in time.
Securing multi-turn conversational language models against distributed backdoor triggers
Tong, T., Xu, J., Liu, Q., and Chen, M · 2024
Closest in time.
Adaptivebackdoor: Backdoored language model agents that detect human overseers
Wang, H., Zhong, R., Wen, J., and Steinhardt, J · 2024
Closest in time.
Badchain: Backdoor chain-of-thought prompting for large language models
Xiang, Z., Jiang, F., Xiong, Z., Ramasubramanian, B., Poovendran, R., and Li, B · 2024
Closest in time.
Xu, H., Sharaf, A., Chen, Y., Tan, W., Shen, L., Durme, B. V., Murray, K., and Kim, Y. J · 2024
Closest in time.
Badrag: Identifying vulnerabilities in retrieval augmented generation of large language models
Xue, J., Zheng, M., Hu, Y., Liu, F., Chen, X., and Lou, Q · 2024
Closest in time.
Backdooring instruction-tuned large language models with virtual prompt injection
Yan, J., Yadav, V., Li, S., Chen, L., Tang, Z., Wang, H., Srinivasan, V., Ren, X., and Jin, H · 2024
Closest in time.
On the vulnerability of safety alignment in open-access LLMs
Yi, J., Ye, R., Chen, Q., Zhu, B., Chen, S., Lian, D., Sun, G., Xie, X., and Wu, F · 2024
Closest in time.
Yi: Open foundation models by 01. ai
Young, A., Chen, B., Li, C., Huang, C., Zhang, G., Zhang, G., Li, H., Zhu, J., Chen, J., Chang, J., et al · 2024
Closest in time.
Negative preference optimization: From catastrophic collapse to effective unlearning
Zhang, R., Lin, L., Bai, Y., and Mei, S · 2024
Closest in time.
Poisonedrag: Knowledge poisoning attacks to retrieval-augmented generation of large language models
Zou, W., Geng, R., Wang, B., and Jia, J · 2024
Closest in time.